MAINTENANCE AND CONDITION MONITORING ON LARGE POWER TRANSFORMERS

Pramod Bhusal (56980W) Lighting Laboratory (HUT)

pramod.bhusal@hut.fi

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CONTENTS

INTRODUCTION 2

TRANSFORMER CONDITION MONITORING ARCHITECTURE AND TECHNIQUES 3

MONITORING BY OIL ANALYSIS 4

WATER CONTENT OF PAPER/OIL SYSTEM 4 ROUTINE TEST OF OIL QUALITY 4 DISSOLVED GAS ANALYSIS 5

PARTIAL DISCHARGE MONITORING 5

TEMPERATURE MONITORING 8

VIBRATIONAL TECHNIQUE FOR MONITORING 8

LOAD TAPCHANGER MONITORING 9

CONCLUSION 10

REFERENCES 11

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INTRODUCTION Large power transformers belong to the most valuable and important assets in electrical power systems. These devices are very expensive and therefore diagnosis and monitoring systems will be valuable for preventing damage to these transformers. Also an outage due to these transformers impacts the stability of the network and the associated financial penalties for the power utilities can be considerably high. So some ways has to be found to avoid sudden breakdown, minimize downtime, reduce maintenance cost and extend the lifetime of the transformer. Condition monitoring is the way helpful to avoid those circumstances and having the capabilities to provide useful information for utilizing the transformer in an optimal fashion. Condition monitoring can be defined as a technique or a process of monitoring the operating characteristics of machine in such a way that changes and trends of the monitored characteristics can be used to predict the need for maintenance before serious deterioration or breakdown occurs, and/or to estimate the machine’s “health”. It embraces the life mechanism of individual parts of or the while equipment, the application and development of special purpose equipment, the means of acquiring the data and the analysis of that data to predict the trends. [1] Before the wide use of Condition monitoring, time-based maintenance had been mainly used maintenance strategy for a long time. Time-based maintenance strategy involves the examination and repair of the machine offline either according to the time schedule or running hours. This strategy may prevent many failures but might also involve many unnecessary shutdowns and unexpected accident in the intervals. This will cause the unnecessary waste of money and time due to the blind maintenance without having much information about the condition of the machine. On the other hand, condition-monitoring lets the operators know more about the state of the machine and indicate clearly when and what maintenance is needed so that it can reduce the manpower consumption as well as guarantee that the running will never halt accidentally. The benefits of condition monitoring can be summarized as:

• Reduced maintenance costs • The results provide quality control feature • Limiting the probability of destructive failures, this leads to improvement in operator

safety and quality of supply • Limiting the severity of any damage incurred, elimination of consequential repair

activities, and identifying the root causes of failures • Information is provided on the transformer operating life, enabling business decisions

to be made either on plant refurbishment or on asset replacement. To be successful, condition monitoring must be self-sufficient and not require manual intervention or detailed analysis. It must be capable of detecting gradual or sudden deterioration and trends and have predictive capabilities to permit alarming in sufficient time to allow appropriate action to be taken and avoid major failure. It must be reliable and not reduce the integrity of the system, it must not require undue maintenance itself and must be a cost effective solution.

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TRANSFORMER CONDITION MONITORING ARCHITECTURE AND TECHNIQUES The integrity of power transformer depends upon the condition of its major components and a weakness in any can lead ultimately to a major breakdown. The main components are the windings, insulation oil, core, bushing and on-load tap changers [2]. The operating temperature of the transformer has a major influence on the ageing of the insulation and the lifetime of the unit. Thermal impact leads not only long-term oil/paper-insulation degradation it is also a limiting factor for the transformer operation [3]. Therefore the knowledge of temperature, especially the hot spot temperature, is of high interest. The degradation of insulation system is accompanied by phenomenon of changing physical parameters or the behaviour of insulation systems. The degradation of insulation system is a complex physical process. Many parameters act at the same time thus making the interpretation extremely difficult. The monitoring and assessment of such components is vital to achieve better reliability of the system

Condition monitoring techniques can be off-line or on-line. Offline techniques can only be carried out during outages and some require complete isolation of the transformer. Frequency response analysis, power factor and capacitance testing and measurement of winding and insulation resistance, magnetizing currents and turns ratios are applicable to large or strategically important transformers. Post fault forensic tests also include paper analysis and metallurgical tests. Online techniques maybe by discrete tests or can be applied continuously and avoid the need of outages. Online, computer based, integrated, multisensor monitoring systems are now commercially available and in development. These online systems monitor important transformer performance including: partial discharge, water-in-oil and thermal performance.

Fig.1 Transformer condition-monitoring techniques [4]

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Figure 1 shows the various techniques for transformer condition monitoring. The systems use a combination of online on-line sensors, computer-generated analysis and predictive data to continuously determine transformer-operating condition and to identify problems at an early stage [5]. MONITORING BY OIL ANALYSIS Insulating oils suffer from deterioration, which can become fatal for transformers. Also, discharge in oil can cause serious damage to the other insulating materials, making the monitoring of power transformers insulation an important task. The traditional way to monitor insulation condition of transformer is by oil analysis and this method is fully covered in international standards [5]. Chemical and physical analysis gives information on serviceability of the oil as both an insulator and coolant and of the transformer with respect to its thermal and electrical properties. Decomposition-products from breakdown of the oil, paper or insulating boards, glues etc are transported through the transformer by the coolant oil. Some are low molecular weight gases dissolved in the oil and can be identified by gas chromatography. Others indicating solid degradations include furans, cresols and phenols and detected by liquid chromatography.

Water content of Paper/oil system The electric breakdown strength of clean oil is little affected by water content until it is nearly saturated. Where such contamination is present, the relative amounts of water and contaminant have a significant, detrimental effect. Few catastrophic failures from arcing in oil occur without free water being present in the oil. However increase in water content in the cellulose (present as paper winding insulation and pressboard mechanical parts) not only increase the chances of a disastrous flashover, but also increases its rate of degradation, with reduction of the mechanical strength and potential failure of this weakest link in a transformer.

Routine test of Oil quality A minimum requirement for any size of oil filled transformer device, to provide a degree of confidence for its continued operation, is analysis of water content, together with electrical breakdown strength and acidity. Electrical breakdown strength test is measured with modern automated test cells. Breakdown voltages are measured with a rising AC voltage under prescribed conditions and the mean of six tests calculated for a single sample of oil. While scatter between the tests can be high, the mean value is reasonably repeatable for duplicate samples. To test Fibre and Particulate content, a simple count of visible fibres is made using a crossed Polaroid viewing system. Large, Medium (2-5 mm) and small (<2 mm) fibres are usually reported. The medium size fibres include the most cellulose fibres derived from the paper insulation whereas the large fibres are usually contaminants introduced either during maintenance or sampling. These fibres, in conjunction with water content, can give an indication of the cause of poor electrical strength. Other routine tests include acidity, resistivity, odour and colour tests. Measurement of the acidity can be done manually or automatically and the level detected either calorimetrically or

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potentiometrically. Water content, acidity and dissolved or suspended contaminants may all individually affect resistivity. So the resistivity test is useful on site as a general indicator of oil condition. Appropriate further tests should be carried out to discover the cause of low values. Odour and colour tests can give an indication of thermal ageing of oil but are only ancillary to quantitative measurement. After a suspected fault, however, the assessment of the smell of oil from different parts of a transformer can be a valuable first indicator of the source and type of fault.

Dissolved gas analysis By analyzing oil sample for dissolved gas content it is possible to assess the condition of the equipment and detecting faults at an early stage. If a fault is indicated, the type of fault can be predicted using various analysis methods. Several dissolved gas analysis (DGA) tests should be taken over a period of time, in order to determine the rate of increase of fault gases, and therefore the rate of advancement of the fault. The gases involved are generally CO, CO2, H2, CH4, C2H4, and C2H6. Further analysis of concentrations, condition and ratios of component gases can identify the reason for the gas formation and indicate the necessity for corrective action.

KEY GAS CHARACTERISTIC FAULT H2 Partial discharge C2H6 Thermal Fault < 300ºC C2H4 Thermal Fault 300ºC- <700ºC C2H2, C2H4 Thermal Fault >700ºC C2H2, H2 Discharge of Energy

Table 1. Key Gas Interpretation Method [6] On-line gas-in-oil monitors became available soon after the introduction of the DGA technology. On-line gas analysis offers the potential for doing a much more revealing assessment of the dynamic conditions inside important transformers than possible through laboratory DGA. An advantage of those on-line monitors is the continuous measurement of one or more gases, so that any gassing trend, which is critical information for incipient fault screening, can be easily obtained. Originally only a hydrogen online monitor was available but now instruments detecting several gases are commercially available with a total oil monitor. The monitors use a combination of on-line sensors, computer generated analysis and predictive data to determine transformer operating condition on a continuing basis and identify problems in the incipient stage. A computer is used for data acquisition and to generate adaptive model-based transformer performance monitoring information. A database is established on important elements of transformer performance and long term trend analysis carried out to “tune” adaptive mathematical models to a particular unit. PARTIAL DISCHARGE MONITORING Dielectric breakdowns in transformers are most frequently proceeded by partial discharges (PD). PD occurs within a transformer where the electric field exceeds the local dielectric strength of the insulation. Possible causes include insulation damage caused by over voltages and lightning strikes, incipient weakness caused by manufacturing defects, or deterioration caused by natural aging processes. Although PD may initially be quite small, it is by nature

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that a damaging process causes chemical decomposition and erosion of materials. Left unchecked, the damaged area can grow, eventually risking electrical breakdown. Dissolved gas analysis (DGA) is routinely employed to detect internal electrical discharging in power transformers. DGA can provide some information about the nature and severity of the PD [7]. However, knowledge of the PD location (which cannot be obtained from DGA results) would be a great help to the engineering specialist who must make decisions about remedial action. Various techniques have been developed to address the problem of PD monitoring in electrical plants. Measuring PD electrically is a very difficult task because the PD signals are extremely small (in the microvolt range), so the electrical interference can limit the sensitivity of the system. Recent years have seen the successful development and application of ultra high frequency (UHF) PD monitoring technology. UHF can be applied to realize not only the PD phenomena but also the location of a PD source. A typical UHF monitoring system is shown in Fig. 2. Signals from one or more sensors are filtered and amplified before they are detected and digitized. Analog-to-digital conversion is increasingly taking place at an earlier stage, as the bandwidth of affordable data acquisition hardware increases. The main reason for this trend is that adaptive digital signal processing can be used to condition the signals dynamically [8].

A clock and a phase reference signal derived from the power frequency waveform provide additional information that is logged with the digitized PD data. Each PD pulse recorded can then be associated with a particular time and “point-on-wave.” The amplitude of the displayed pulses is proportional to the energy of the UHF signal. Neural networks or intelligent software agents can be used to recognize patterns in this data and provide meaningful information concerning the nature and characteristics of the PD source. The key technique of UHF method is sensor and sensitivity. The sensor mainly adopts the apacitive sensor or UHF antenna. Because of the broad frequency content of the actual discharge, capacitive coupling in the UHF region has been shown to be an effective under certain conditions [9]. Scottish Power and Strathclyde University have developed a diagnostic tool for transformers, which uses UHF couplers operating in the 300-1500 MHz band [10]. The approach taken was to adapt technologies that were developed for continuous partial discharge monitoring in gas insulated substations (GIS). Principles such as pattern recognition and time-of-flight measurement are well known in relation to GIS, but involve greater challenge when applied to transformers.

Fig. 2 Principles of a typical UHF PD monitoring system

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Acoustic PD monitoring has been another interesting technique getting the focus from both academic and industrial people for many years. Partial discharges occurring under oil produce a pressure wave that is transmitted throughout the transformer via the oil medium. Technique is available in which piezoelectric sensors are connected to the outside of the tank to measure the acoustic wave impinging on the tank either directly or via wave-guides. The advantages of acoustic method are that firstly, it can reach the possibility of PD location, which is of considerable value for power equipment maintenance. Secondly, it could recognize the PD acoustic signal regardless the electromagnetic noise in substation. Sometimes, it is difficult to discriminate the PD acoustic signals due to the interferences from either electrical or mechanical sounds in the substation. This is the obstacle for the method to be widely applied. The use of a new fibre optic acoustic sensor for the detection of discharges from within the transformer is developed by Virginia Polytechnic Institute and State University. The basic principle of the developed sensor is illustrated in figure 3. The system involves a sensor probe, optoelectronic signal processing and an optical fibre linking the sensor head and the signal-processing unit. The light from a laser diode is launched into a tow-by-two fibre coupler and propagates along the optical fibre to the sensor head. As shown in the enlarged view of the sensor head, the lead-in fibre and the silica glass diaphragm are bonded to form a cylindrical sensor-housing element. The incident light is first partially reflected (-4%) at the end face of the lead-in fibre. The remainder of the light propagates across the air gap to the inner surface of the diaphragm. The inner surface of the diaphragm is coated with gold, which reflects the entire incident light (96%), preventing any reflection from the outer surface; the fibre sensor is thus optically self-contained in any environment. This means that the optical signal is only a function of the length of the sealed cavity; and it is immune to the diaphragm outer surface contamination resulting from the contact with transformer oil. As indicated in the enlarged view of the sensor head, the diaphragm is tilted at an angle with respect to the lead-in fibre end-face so that the fibre captures only about 4% of the second reflection. The two reflections travel back along the same lead-in fibre through the same fibre coupler to the photo-detection end. The interference of these two reflections produces sinusoidal intensity variations, referred to as interference fringes, as the air gap is continuously changed. The development of the diaphragm pressure Sensor was concentrated upon utilizing an epoxy to bond the silica hollow core tube to the ferrule and the hollow core to the silica diaphragm. Using an online monitoring process, the air gap between the fibre and the inner surface of the silica diaphragm was adjusted to give the highest interference fringe visibility.

Fig. 3 Illustration of the principle of the fiber optic acoustic sensor

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TEMPERATURE MONITORING Monitoring a transformer through temperature sensors is taken as one of the simplest and most effective monitoring technique. Abnormal temperature readings almost always indicate some type of failure in a transformer. For this reason, it has become common practice to monitor the hot spot, main tank, and bottom tank temperatures on the shell of a transformer. As a transformer begins to heat up, the winding insulation begins to deteriorate and the dielectric constant of the mineral oil begins to degrade. Likewise, as the transformer heats, insulation deteriorates at even a faster rate. Monitoring the temperature of the load tap changer (LTC) is critical in determining if a LTC would fail. In addition to the LTC, abnormal temperatures in the bushings, pumps, and fans can all be signs of impending failures. Recently, thermography has been used more widely for detecting temperature abnormalities in transformers. In this technique, an infrared gun is taken to the field and used to detect temperature gradients on external surfaces of the transformer. Infrared guns make it easy to detect whether a bushing or fan bank is overheating and needs to be replaced. The method is also useful in determining whether a load tap changer (LTC) is operating properly. Thermography is effective for checking many different transformers quickly to see if there is any outstanding problem [12]. However, thermography is not conducive to on-line measurements and; therefore, is prone to miss failures that may be developing between the periods when the transformers are checked. In order to make on-line monitoring possible, thermocouples are placed externally on the transformer and provide real-time data on the temperature at various locations on the transformer. In many applications, temperature sensors have been placed externally on transformers in order to estimate the internal state of the transformer. These temperature readings can be used to determine whether the transformer windings and oil are overheating or running at abnormally high temperatures. High main tank temperatures have been known to indicate oil deterioration, insulation degradation, and water formation [12]. VIBRATIONAL TECHNIQUE FOR MONITORING The diagnostic methods described so far have all dealt with trying to detect a failure in the electrical subsystem of the transformer, namely the electrical insulation around the coils. There has also been research carried out in regards to mechanical malfunctions in a transformer. According to the study, the factors generating the vibration in the transformer are of two types: core vibration and winding vibration [13]. Core vibration consists of excitation by magnetostriction and excitation generated at air gaps. Winding vibration is generated by Lorenz force due to correlation of leakage flux and winding current. These vibrations from winding and core penetrate into transformer oil, travel through it and reach the tank walls exciting their oscillations. Based on this analysis, we can say that tank vibration signals have strong relation with the condition of transformer’s core and windings and can provide useful diagnosis information. The vibrations from the windings and core can be measured at the tank wall by piezoelectric accelerometers. The accelerometer is positioned at different locations on the tank and measurements are taken twice in no-load and loaded modes, which is necessary to separate the vibrations of the core and windings. In no-load mode the electrodynamic forces in the

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windings are practically absent, vibrations can be attributed to magnetic core conditions only. Measurements taken under loaded conditions include both core and coil vibrations. Therefore it is possible to find the spectrum related to winding vibration by subtracting the no-load (core) results from the loaded (core and coil) results. Such an approach is justified because the magnetic flux in the core is almost independent of the load. The vibration spectra consist of harmonics besides fundamental frequency (two times of power frequency).

Fig 4. Example of the measuring system

The core vibration will aggravate when core-clamping force is loosing. If high-voltage winding or low-voltage winding has displacement, distortion or lack in clamping force, the difference in height between the winds will increase, thus leads to ampere-turn imbalance and axial force deviation, resulting in intensified vibration. Acceleration sensors, which can be used to measure the vibration, are divided into piezoelectric type, train type and servo type. Low frequency response of servo-type acceleration sensor is excellent but its bandwidth is narrow (<500Hz), obviously not suitable for tank vibration measurement. Comparing piezoelectric-type with train type, piezoelectric-type sensor has wider application, installation resonance frequency is beyond 100Khz, and the bandwidth margin is ample. LOAD TAPCHANGER MONITORING On-load tapchangers (OLTCs) are one of the most problematic components of power transformers. The majority of transformer failures are directly or indirectly caused by tap changer failures, because a tapchanger contains the only moving components associated with transformer. The cost of tapchanger is very low compared to that of transformer but the failure of the tap changer can be responsible for the destruction of the complete unit. In earlier days the only method used by the tapchanger manufactures was the fitting of a temperature probe to monitor the temperature of the diverter switch oil. This proved totally inadequate, because the probe took time to register any significant change in temperature. Recently surge relays have been fitted to both the selector and diverter switch components but

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they are still slow to respond. The diverter switches have to be carefully set up to avoid any spurious operation of the surge relays. On-line monitoring systems for on-load tapchangers are now available. In one on-load tapchanger diverter switch protection scheme a current transformer (CT) is mounted in the diverter switch compartment to monitor current passing through the transitional resistors of the tapchanger during operation. The output from the CT is fed to a timed relay, which operates if the duration of the current flow exceeds a preset limit. The CT is placed in such a way that it will be energized whenever current passes through either or both of the resistors during operation of the tapchanger, The output of the CT is used to energize a current transducer, and this in turn operates a time delayed relay. CONCLUSION The justification for condition monitoring of power transformers is driven by the need of the electrical utilities to reduce operating costs and enhance the availability and reliability of their equipment. Condition monitoring produces reliable information on plant condition, which allows maintenance resources to be optimized and assist with optimum economic replacement of the asset. Many techniques for the monitoring are available and new techniques are being developed constantly. Researches are concentrated on computer-based techniques on online monitoring of transformer and it’s components.

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REFERENCES

1. Y. Han and Y.H. Song, Condition Monitoring Techniques for Electrical Equipment – A Literature Survey, IEEE transactions of power delivery, vol. 18, No. 1, January 2003

2. Muhammad Arshad, Syed M. Islam, Power transformer condition monitoring and assessment for strategic benefits. Australian Universities Power Engineering Conference ‘AUPEC2003’, 28September- 1 October 2003.

3. IEC Loading guide for oil immersed transformers. IEC Standard 60354, Sep.1991. 4. J. C. Steed, Condition monitoring applied to power transformers: an REC View. IEE

conference on ‘The reliability of Transmission and Distribution Equipment’, Conference Publication No. 406, pp 109-111, March 1995.

5. D. Harris, M. P. Saravolac, Condition Monitoring in Power Transformers. IEE colloq.

Condition Monitoring of Large Machines and Power Transformers, 1997, pp. 7/1–7/3. 6. Pahlavanpour, B.; Wilson, A.; Analysis of transformer oil for transformer condition

monitoring. IEE Colloquium on Engineering Review of Liquid insulation. 7 Jan. 1997 Page(s):1/1 - 1/5

7. M. Wang, A. J. Vandermaar, and K. D. Srivastava, “Review of condition assessment

of power transformers in service,” IEEE Elect. Insul. Mag., vol. 18, no. 6, pp. 12–25, Nov/Dec 2002.

8. Judd M D, Yang L and Hunter I B B 2005 Partial discharge monitoring for power transformers using UHF sensors 1: sensors and signal interpretation IEEE Ins. Mag. 21 5–14

9. Judd, M:D:, B. M. Pryor, S. C. Kelly and B. F. Hampton, Transformer monitoring using the UHF technique, Proc. 11th int. Symp. on High Voltage Engineering (London), Vol. 5, pp. 362-365, August 1999.

10. Judd, M. D, B.M. Pryor, O.Farish, J.S.Pearson and T. Breakenridge, Power Transformer Monitoring Using UHF Sensors. IEEE International Symposium on Electrical Insulation.April, 2000

11. Ward, B.H.; Lindgren, S. A survey of developments in insulation monitoring of power transformers. Conference Record of the 2000 IEEE International Symposium on Electrical insulation, 2000, Page(s): 141-147

12. Kirtley Jr., J.L., Hagman, W.H., Lesieutre, B.C., Boyd, M.J., Warren, E.P., Chou, H.P., and Tabors, R.D. 1996. Monitoring the Health of Transformers. IEEE Computer Applications of Power, 63, pp.18-23.

13. Ji Shengchang; Shan Ping; Li Yanming; Xu Dake; Cao Junling; The vibration

measuring system for monitoring core and winding condition of power transformer. Proceedings of 2001 International Symposium on Electrical Insulating Materials, 19-22 Nov. 2001, Page(s):849 - 852

AGEING PHENOMENA OF PAPER-OIL INSULATION IN POWER TRANSFORMERS

Henry Lågland University of Vaasa

Henry.lagland@uwasa.fi

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Contents 1. INTRODUCTION 2. MECHANISM OF DEGRADATION OF THE PAPER-OIL INSULATION IN POWER TRANSFORMERS 3. THERMAL AGEING PROCESSES 3.1 Thermal ageing of paper-oil insulation 3.1.1 The ageing phenomena 3.1.2 Degree of polymerisation (DP) 3.1.3 Mathematical models for thermal ageing 3.1.4 Thermal ageing of oil/transformerboard insulation systems 3.1.5 Thermal ageing of solid and liquid insulating materials under the influence of water and oxygen 3.1.6 Comparison between open and closed expansion system 3.1.7 Comparison between constant ageing temperature and cyclic temperature changes 4. ELECTRICAL AND COMBINED ELECTRICAL AND THERMAL AGEING 3.1 Electrical ageing 3.2 Combined electrical and thermal ageing 5. CONCLUSION Literature

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1. INTRODUCTION Power transformers are used in power generation units, transmission and distribution networks to step up or down the voltage of the power system (figure 1). The capacity is usually between a few MVA to about 100 MVA. To be able to use the real capacity of power transformers it is important to know the duration and level to which power transformers can be thermally stressed. Thus increasing demands are being imposed on the liquid and solid insulating materials with regard to the operating reliability and overloading capability. This paper describes the mechanism of degradation of the paper-oil insulation in power transformers. Mathematical models for thermal ageing are briefly presented as well as findings on thermal and electrical ageing phenomena for liquid cooled transformer insulation systems. Figure 1. Power trans- former (ABB). The paper is mainly based on material chosen by Dr. Hasse Nordman, who is is chairman of the working group of the loading guide for oil-immersed power transformers [3]. Most of the material in this paper is based on the chapter Transformerboard II, Properties and application of transformerboard of different fibres by H.P. Moser and V Dahinden [1]. 2. MECHANISM OF DEGRADATION OF THE PAPER-OIL INSULATION IN POWER TRANSFORMERS Paper is a sheet of material made from vegetable cellulose fibres dispersed in water. The fibres are drained to form a mat. Cellulose is a linear polysaccharide consisting of hydro D-glucopyranose units held together by a β-linkage. A single cellulose fibre consists of many of these long chains [2].

Figure 2. Cellulose structure [2] The condition and strength of the fibres themselves and the physiochemical bonding, known as “hydrogen bonding”, between the cellulose molecules are the most significant factors that influence the strength of a dried sheet of paper (figure 2). The ageing performance of a sheet of paper is influenced by the degradation of the cellulose. The mechanism of degradation is rather complicated and depends on the environmental conditions. According to [2] there are three types of degradation:

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1. Hydrolytic degradation, which refers to the cleavage at the glycoside linkage giving the sugar glucose

2. Oxidative degradation. Because cellulose is highly susceptible to oxidation, the hydroxyl groups are the weak areas where carbonyl and carboxyl groups are formed eventually causing secondary reactions giving chain scission

3. Thermal degradation below 200 °C is similar to, but faster than, the normal ageing of cellulose. Oxidative and hydrolytic degradation occur giving:

-severance of the chain, reducing the degree of polymerization and the strength -opening of the glucose rings

Decomposition products are mostly water and carbon oxides. Processes have been developed to improve the resistance of paper to degradation, or “upgrade” it. This is done either chemically by converting some of the OH radicals to more stable groups or by adding “stabilizers”, such as nitrogen-containing chemicals like dicyandiamide. The ageing of oil/solid insulation systems can be influenced by the treatment of the components of the insulation system, addition of inhibitors and sealing of the insulation system. 3. THERMAL AGEING PROCESSES The ageing behaviour of oil/solid insulation systems depends on the thermal, mechanical, electrical and combined electro-thermal stresses of the power transformers. 3.1 Thermal ageing of paper-oil insulation 3.1.1 The ageing phenomena [1] The ageing process in the oil/cellulose insulation system under thermal stress and their measurable effects are due to chemical reactions in the dielectric. Cellulose is a linear macromolecule, which in the unaged state consists of 1000- 3000 glucose rings (figure 3).

Figure 3. Basic structure of the cellulose macromolecule [1].

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The periodically repeating structural units of the macromolecule, the β-glucose rings, are bonded to one another via oxygen bridges between the first and fourth carbon atom. Via the hydroxyl groups cross links are formed to crystalline regions, the micelles. Between the micelles individual cellulose molecules accumulate, forming a cavity system with a capillary diameter of 10 nm to a few µm within the fibre. The fibre length of pine pulp varies between 0.25 mm and 4 mm. The insulating oil consists principally of paraffins, naphthenes and a small portion of aromatics. The chemical composition of the mineral oil can only be stated approximately since the oil consists of a mixture of hydrocarbon compounds with different molecular structures. The main important parameters affecting the ageing of the solid and liquid insulation are:

1. The temperature of the oil/cellulose dielectric 2. The presence of water 3. The presence of oxygen

The temperature of the oil/cellulose dielectric is the critical ageing parameter for the change in the mechanical and electric properties of the material. Thermally supplied vibration energy of many atoms and groups of atoms is temporarily concentrated on individual C-H, C-O and C-C bonds and cleaves these bonds. This results in cleavage products such as carbon dioxide, carbon monoxide, water, hydrogen and scarcely measurable amounts of methane. By interacting with the oil components the entire final molecule can be separated off at the end of a chain and converted to other substances (e.g. sludge, acid). Also the ageing of oil under high thermal stress is characterized by chemical reactions. For the stability of the oil molecules the bond energy of the C-H and C-C single and double bonds is critical. Water forms as a reaction product, both during the thermo kinetic degradation of the cellulose and during the ageing of the oil. Apart from the thermo-kinetic degradation, the moisture present at the beginning of the ageing process, as well as the water formed by the reactions of the cellulose and of the oil, causes additional decomposition of the chain molecules. Because of the hydroscopic nature of the cellulose and fibre structure (capillaries), the water molecules accumulate between the cellulose chains and thus promote their thermo hydrolytic degradation. The water continuously causes fresh molecular cleavage thus having the negative property of constantly and retroactively accelerating the ageing process of the cellulose. In contrast to ageing of the cellulose, ageing of the oil is scarcely affected by water. Oxygen is predominantly present in the oil and thus noticeably accelerates the ageing of the oil while the effect of the oxygen on the ageing of the cellulose tends to be more moderate. On the other hand, in thoroughly dried insulation systems and in particular in the presence of fairly large amounts of oxygen, oxidation is the dominant process in the ageing of oil, at least in the initial stage. In the presence of reactive substances, in particular oxygen, the cleavage of oil molecule bonds is followed by a reaction sequence, the principal oxidation products of which are acids, solid constituents (sludge), water, carbon dioxide and carbon monoxide. Dissolved metals, such as iron and copper have a catalytic effect on the degradation process of the oil molecules. Since no free oxygen is formed during the ageing process of either the oil or the cellulose, oxygen enters the system only from the outside. Since the degradation of oil molecules produces other reactive substances, the decomposition of the oil molecules continues even where there is a deficiency of oxygen once the decomposition mechanism has been initiated.

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3.1.2 Degree of polymerisation (DP) [1] The progress of the ageing of the oil/cellulose dielectric, the presence of water and the oxidation by oxygen, can be determined from the change in the material properties and from the formation and precipitation of reaction products. The degree of polymerization is the connection between the deterioration in the material properties and the formation of ageing products. It is a direct indication of the decomposition of the cellulose macromolecule and proves to be the most informative parameter for assessing the ageing or the progress of ageing of the cellulose (figure 4).

Figure 4. Degree of polymerisation (DP) [1]. 3.1.3 Mathematical models for thermal ageing [1] In 1930 Montsinger stated a law describing the existing interrelation between life expectancy and operating temperature of the transformer based on measurements on transformer insulating materials. Montsinger´s law states that an increase or decrease of the operating temperature by 6-10 K, according to the insulation raw material, results in the doubling or halving of the ageing rate. Using the Arrhenius relations Büssig and Dakin formulated a life law derived from the reaction equations of the chemical principles. By inserting the material characteristics in place of the molecule concentrations in the chemical reaction equations the thermo-kinetic deteriorations in characteristics are described.

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The following differential equation for the first order reactions is:

( ) ( ) xTCxxdtd

×=−0 (1)

0x = material characteristic at time t=0 (initial value)

t = time in days x = material characteristic after ageing to time t C = ageing rate constant [1 / days] T = absolute temperature in K As is a constant the differential equation (1) can be reduced to: 0x

( ) xTCdtdx

×−= (2)

The following reduction in physical characteristic x subjected to thermal load T is produced by solving differential equation (2) with boundary condition x ( = 0) = : t 0x ( ) ( )[ tTCxTx ×−×= exp0 ] (3)

The differential equation (2) was modified by Dakin by introducing the general order of reaction α. The differential equation (4) is normalized with the initial value x ( t = 0) = : 0x ( ) ( ) 0xtxtX = ( ) αXTCdtdX ×−= (4)

α = order of reaction (α > 0) Taking into account the order of reaction α with boundary condition x ( = 0) = 1, the solution functions of differential equation (4) are:

t

for α = 0 ( ) ( ) tTCtX ×−= 1 for α = 1 [see (2)] ( ) ( )( )tTCtX ×−= exp (5) for α > 1 ( )( ) ( ) ( ) tTCtX ××−=− 11 1 αα Arrhenius equation can express the temperature dependence of ageing rate constant : ( )TC ( ) ( )( TRECTC A ×−×= exp ) (6)

AC = constant [1 / time]

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E = activation energy [J / mol] R = gas constant [J / K mol] Or with Montsinger´s empirical formula: ( ) ( )ϑϑ ××= mCC M exp (7)

MC = constant [1 / time]

m = constant [1 / °C] ϑ = temperature in °C The so-called Montsinger step mM 2ln=∆ϑ (8) Deterioration in the physical characteristics of thermally stressed oil/solid insulation systems as a function of time can be described by means of the relation (3) and (5). The temperature influence (ageing parameter) is mathematically expressed with equations (6) and (7). 3.1.4 Thermal ageing of oil/transformerboard insulation systems [1] In the following the ageing behaviour of TRANSFORMERBOARD T III (density = 0.84 g/cm3) and T IV (density = 1.18 g/cm3) is described taking into account interactions with mineral oil as a liquid medium. The results are from measurements on testing rigs fitted with an open expansion system which were operated at temperatures of 90 °C, 105 °C, 120 °C and 135 °C with cycles. T III and T IV are made from the same raw material but produced by different processes. T IV is extremely highly compressed by hot pressing and T III rather less so by calendaring.

Figure 5. Degree of polymerization and normalized tensile strength (δ / δ0) as a function of time [1].

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The almost identical reduction in degree of polymerization for the two materials indicates that the production process has little influence on the ageing, respectively on the breakdown of the cellulose macromolecules (Fig. S.12). The superior ageing behaviour of T IV compared with T III reveals when examining the mechanical characteristics. The higher density resulting from hot pressing and the greater degree of cross linking with its associated reduction of free fibre surface provides T IV with improved thermal stability with respect to the mechanical characteristics (Fig. S.14) No deterioration of the dielectric strength of the solid samples occurred under thermal stressing (Fig. S.18). The conductivity or specific resistance of the oil/solid dielectric is not a characteristic of the molecular structure of either cellulose or oil, but is due to ionic by-products. Ions are present even in unaged, freshly prepared samples, both in the oil and the solid insulation, in the form of residues from the production process. Additional ionic decomposition products are produced during oil and cellulose ageing by the high thermal stressing of the insulation system. Figure 6. Impulse withstand field strength as a function of ageing time [1]. Fig. S.20 shows that the ageing temperature plays an important part in lowering the specific resistance. The alternating voltage losses are also principally due to the ion condition. Hence the increase in loss factor tan δ of the solid samples over time with temperature as a parameter is caused by an increase in the ion concentration (Fig. S.19). This is on the one hand caused by dissociated ageing products from the chemical reactions of the oil and cellulose and also their interaction and on the other hand by the impurities and decomposition products absorbed from the fluid medium which increase tan δ.

9

Figure 7. Electric characteristics (impulse withstand field strength, loss factor and specific resistivity) of oil impregnated solid samples as a function of ageing time [1]. Gases produced in the system by the thermo-kinetic breakdown of the cellulose macromolecules and the decomposition of the oil molecules due to ageing, also the interaction between the reaction products of the solid and liquid insulation, are carbon dioxide CO2, carbon monoxide CO, hydrogen H2 and scarcely measurable amounts of aliphatic hydrocarbon gases. Water H2O is also produced and entire sections can be split off the end of the cellulose macromolecules. These separated molecules convert to other substances which are observable in the oil in the form of acids or low molecular sludge. Fig. S.21 shows the increase in water content in the solid samples as a function of time and at different temperatures. Shown in Fig. S.22 is the increase in water content in the oil which forms the insulation system together with T IV or T III. In the oil/solid insulation system the law applies that the relative moisture content in the oil must be identical to the relative moisture content in the board if the diffusion processes are complete. Since the absolute water content in the sheet material is far greater than in oil, the relative moisture content of the system is mainly determined by the solid sample. The saturation moisture of the oil and also that of the board is highly temperature dependent and moreover in opposition. Cyclic operating temperatures create non-equilibrium of the moisture content or promote equalizing processes between the solid samples, the oil and the air. With a reduction of the operating temperature, the solid sample absorbs water and releases it again during heating up.

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Figure 8. Water content of the solid sample and oil as a function of time [1]. The most important electrical characteristics of the liquid insulation which in the transformer forms an inseparable insulation system with the solid samples are the breakdown voltage, loss factor, specific resistance and dielectric constant of the oil. The breakdown voltage of the oil, which has been aged together with T IV or T III, is principally dependent on the water content of the liquid medium (Fig. S.9). If the water is dissolved in the oil, i.e. the solubility limit of water in oil at the relevant temperature is not yet reached, then the breakdown voltage is approximately 65 kV, regardless of the ageing temperature, the ageing time and the composition of the insulation system. If the water is emulsified in the oil, then the breakdown voltage falls to 25 kV, the undissolved quantity of water having no further influence. Both the increase in loss factor (Fig. S.25) and the reduction in specific resistance (Fig. S.26) are relative small in comparison to the loss factor (Fig. S.6) and specific resistance changes (Fig. S.7) of the oil aged under the same conditions without the addition of the solid samples (pure oil ageing). This is because the T IV and T III absorb dissociated particles from the oil together with ionic decomposition products from the ageing process, thus exerting a cleansing effect. The TRANSFORMERBOARD reduces the direct and alternating current losses of the oil by approximately a factor of 10.

11

Figure 9. The loss factor and the specific resistivity of the oil as a function of ageing time and breakdown voltage of the oil as a function of the water content [1].

12

3.1.5 Thermal ageing of solid and liquid insulating materials under the influence of wateand oxygen [1]

r

g. S.30 show the effects of intentionally added water and the influence of the ontinuous extraction of water from the insulation system on the ageing process of T IV and the

and DP and tensile strength (Fig. .30) as a function of time [1].

Fig. S.29 and Ficoil. In Fig. S. 29 is the water and carbon dioxide production as a function of time plotted. They are both relevant values for assessing the ageing process. In Fig. S.30 are the representative degree of polymerization for the cellulose ageing behaviour and the tensile strength δz, which indicates the deterioration of the mechanical characteristics. Both the added water and the water formed by the reactions of the oil and cellulose due to ageing, retrospectively accelerate the ageing of T IV. This means that the life expectancy of poorly dried transformers can fall sharply compared to correctly prepared transformers. This is also confirmed by the results of experiments in which oil was constantly dried throughout the ageing period by means of molecular sieves. A life expectancy of double or even more is possible if the oil of a normally operated transformer is permanently dried and degassed during its total operating time. Figure 10. Production of water and carbon dioxide (Fig. S.29)S

13

As can be seen in Fig. S.33a the tensile strength, elongation and DP are only slightly influencedduring the experiments with a mo

lecular sieve as acceptor in the system constantly degassing and

rying the oil and thus, by virtue of equilibrium, also dehydrating the solid insulation. Adding

e experiments with xygen is due to the filtering effect of the NBC on the oil (Fig. S.34c). NBC proves to be an

b) Without molecular sieve

c) tion

d) r addition (50 ml)

e) 0 ml) and oxygen

ormerboard T IV/oil insulation (Fig. S.33), change in oil properties (Fig. S.35) as a

doxygen to the oil/cellulose insulation system significantly increases the water content in both the solid sample (Fig. S.33c) and the liquid medium (Fig. S.35c). The large rise in water content of the system is caused by the reactions of the oil molecules with oxygen. The loss factor reduction in the molecular sieve experiments shows the cleansing effect of the zeolites on both the solid samples (Fig. S.33a) and the oil (Fig. S.35a). An extremely large increase in loss factor measured at 90 °C occurs when particular board water content is exceeded (Fig. S.33d). The mechanical characteristics of NBC remain largely unaffected with a thermal stress at 135 °C over an ageing period of 100 days (Fig. S.34). The increase in loss factor in thoextremely ageing resistant material.

a) Addition of molecular sieve 5 Å [+ MS 5Å]

[-] Oxygen addi[+O2] Wate[+H2O] Water (5addition [+H2O+O2]

Figure 11. Change in solid properties of TransfNomex Board NBC/oil insulation (Fig. S.34) and function of time [1].

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3.1.6 Comparison between open and closed expansion system [1] Fig. S.38 shows the effects of open and closed expansion vessels on the change in solid and oil haracteristics. The test chambers were operated at either a cyclically changing or a constant

the open system. On the other hand the water content of the board increases lightly more in the closed system than in the open. In the open vessels, with advanced ageing,

ctemperature. The tensile strength of T IV and the degree of polymerization, not plotted but running qualitatively parallel, which characterize the ageing state of the cellulose, fall less sharply in the closed than insthe extraction of water from the system can be achieved via the air cushion between the oil in the expansion vessel and the silica gel drier, supported by the temperature cycles which generate a regular air exchange. In long term tests at constant temperature, the tensile strength of the T IV decreases more sharply than in the experiments with cyclic temperature changes. The greater loss is mainly caused by the higher water concentration in the board which accelerates the thermo-hydrolytic decomposition of the cellulose. In the open system the oxygen produces accelerated oil ageing, in which, together with many other reaction products, ions occur, increasing the loss factor.

Figure 12. Change in Transformerboard T IV properties as a function of time [1].

.1.7 Comparison between constant ageing temperature and cyclic temperature changes

he tests also examined the influence of temperature cycles on the ageing behaviour of the mixed dielectric. The load cycles of a realistically loaded transformer were simulated by the daily

3 [1] T

15

recurring two hour cooling and subsequent three hour heating periods of the test vessel to thn

e ominal temperature. In the long term tests at constant temperature, the tensile strength of the T

decreases more sharply than in the experiments with cyclic temperature changes (Fig. S.38).

.1 Electrical ageing [1]

s of .33 kV/mm, 6.66 kV/mm and 10 kV/mm, on the ageing characteristic of the oil/solid dielectric. ransformerboard T IV together with inhibited oil was tested in the test vessels at room

purely electrical ageing.

lose molecules. In the transformer, where e insulation is exposed to continuous electric stresses with a maximum value of 3…4 kV/mm,

C were investigated at constant operating mperatures of 135 °C (120 °C) and a continuous electric stress with field strength of 5 kV/mm. he two solid materials were tested with mineral oil with or without a molecular sieve (Fig.

g of 100 % Aramid synthetic fibre.

. The st was discontinued. The increases found during ageing are a direct consequence of the reaction

IVThe greater loss is mainly caused by the higher water concentration in the board which accelerates the thermo-hydrolytic decomposition of the cellulose. The temperature cycles cause a regular exchange between the solid samples and the liquid insulation, also between the oil in the expansion vessel and the air cushion dehumidified by the silica gel drier. Hence the temperature cycles facilitate the extraction of water from the system, if only in small quantities. 4. ELECTRICAL AND COMBINED ELECTRICAL AND THERMAL AGEING 4 The aim of these tests was to determine the effect of AC electric fields (50 Hz) with strength3Ttemperature corresponding to During the tests no changes were observed which would indicate ageing of the samples. The constancy of the loss factor indicates that, during the 6000 hours test time, no ionic ageing products which increase tanδ were formed. Consequently, continuous electric field strengths of ≤ 10 kV/mm are incapable of cleaving either oil or celluththe electric field has little direct effect on ageing. 4.2 Combined electrical and thermal ageing [1] Transformerboard T IV and Nomex Board NBteTS.52). NBC is a highly compacted pressboard, consistin The loss factor of the oil/NBC insulation system remains unaffected during the entire test period under a stress of 5 kV/mm and 135 °C. In contrast to the tests with NBC, the oil/T IV insulation shows a marked increase in the loss factor. During the experiment on the oil/T IV insulation without a molecular sieve, partial discharges were observed after 2500 hours of operationteproducts which are formed by thermo-kinetic cleavage (135 °C) of the cellulose and oil molecular chains and their interactions. After the oil had been cleaned and degassed the experiment was continued with the same samples at a temperature of 120 °C. The abrupt decrease in the loss factor of the oil/T IV dielectric from 130 ‰ to 40 ‰ can be explained mainly by the experiment arrangements and the measurement technique. The loss factor measured at 120 °C after this operation decreased from 40 ‰ to 30 ‰ during the remaining 3500 hours. The reason for this decrease in the loss factor is the reduced rate of formation of ionic and gaseous reaction

16

products. In the investigations it was found that there is a change in the reaction mechanism in the ageing process of the T IV in the temperature interval between 120 °C and 135 °C. The molecular sieve degasses the oil permanently and thus promotes diffusion of the ageing products out of the solid sample. A prediction for an increase in the loss factor is a high temperature. This is also confirmed by the electrical tests at room temperature where no increase in loss factor was observed even at field strengths of 19 kV/mm over a test period of 6000 hours.

he electric field strength therefore has only an indirect effect on the ageing of the cellulose/oil

a function of time [1].

sulations with Kraft cellulose as a basic material, a similar decomposition process of the chain olecules was observed during thermal stressing in spite of their different production methods.

hig nce of T IV compared to T III became apparent only on comparison of e relative reduction in mechanical strength. At temperatures over 120 °C, the ageing rate of T

Tinsulation system by separating the ions formed by the supply of thermal energy and thus preventing them from recombining.

Figure 13. Variation of the loss factor of oil/T IV and oil/NBC dielectric as 5. CONCLUSION For the hot pressed Transformerboard T IV and calendared pressboard T III, both solid inmThe her ageing resistathIV is almost twice as high as that below 120 °C. The ageing rate of T IV is highly susceptible to the presence of water in the oil/solid insulation system at high thermal stresses, whilst the oil ageing is hardly changed by moisture. The presence of oxygen in the oil/cellulose insulation systems produces a severe ageing of the oil, but only slight ageing of the T IV. In the open expansion system, the ageing of the oil and cellulose solid insulation in interaction with the oil is accelerated by the admission of oxygen, supported by the cyclic temperature changes. An

17

excellent age stabilizing effect on Transformerboard T IV and the oil was achieved by the utilization of a molecular sieve in the oil/cellulose insulation system, principally by the adsorption of water and gas in connection with a hermetically sealed system. Nomex Board is very resistant to high thermal stresses, even with the additional influence of water and oxygen which had a significant effect on the ageing of the oil/cellulose insulation system [1]. The ageing is influenced not only by the temperature. Also the humidity, acid and oxygen content have a dramatic impact of the ageing. To get the influence of these parameters on the loading capability of power transformers Dr. Hasse Nordman has initiated a Cigre working group with a task list according to [8]. Another task for the working group is to define the content of humidity, cid and oxygen near the hot spot of the windings of the power transformer. If the working group

Transformerboard II, Properties and application of transformerboard of different fibres. Publisher Weidmann. 1987.

Shroff, A.W. Stannett. A review of paper ageing in power transformers. 1985.

] IEEE C57.91-1995. Annex D. Philosophy of guide applicable to transformers with 55 °C

] L.E. Lundgaard, W Hansen, D Linhjell, T.J.Painter. Ageing of Oil-Impregnated Paper in , vol. 19, No 1, January 2004.

cope. 2005.

afinds these base values, the article by Lundgaard, Hansen, Linhjell and Painter [7] can give the relevant factors for the ageing speed. Literature [1] H.P. Moser, V Dahinden. [2] D.H. [3] IEC 60076-7. Loading guide for oil-immersed power transformers. Proposal 2005. [4 average winding rise (65 °C hottest-spot rise) insulation systems. [5] IEEE C57.91-1995. Annex I. Transformer insulation life. [6] Cigre WG. Relative ageing rate and life of transformer insulation. [7 Power Transformers. IEEE Transactions on Power Delivery [8] Cigre WG Relative ageing rate and life of transformer insulation. S

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ON-LINE MONITORING APPLICATIONS FOR POWER TRANSFORMERS

Pekka Nevalainen Tampere University of Technology

pekka.nevalainen@tut.fi

ABSTRACT Power transformers are very critical components in an electrical network. To provide continuous power supplying, transformers need to operate without failures. Condition monitoring of power transformer is a good tool to reduce power outages caused by transformer failures. There are many various methods for condition monitoring. This paper describes briefly several different types of applications for on-line monitoring. INTRODUCTION Modern power transformers are expected to operate very long and even a lifetime of 40 years may be expected. To provide long lifetime and to avoid power outages, condition monitoring of the transformers should be implemented. Furthermore, condition monitoring can reduce maintenance costs, may be used for identifying the reasons for failure and extend the lifetime even more [1], [2]. Condition monitoring can be carried out using different methods. Generally the methods can be divided in two groups; off-line and on-line. Off-line methods usually demand disconnecting the transformer from electric network and may use intrusive actions to do the needed measurements. These methods include for example: return voltage measurements (RVM), dielectric frequency response (tan-δ (f)) and gas analysis of transformer oil sample [2]. These methods and measurements are usually very accurate and provide good information of transformer’s condition [3]. On-line methods are using different type of sensors attached to the transformer. These sensors won’t affect the normal operation of the transformer, thus making it possible to perform continuous measurements for long periods of time. The on-line methods are based on sensors, data acquisition and analysis. Some of the methods are still experimental, but good commercial solutions exist [3]. On-line monitoring applications for power transformer include for example: measurements of different temperatures, gases in oil, humidity of the oil, partial discharges, winding movement, furfuraldehyde, tan-δ and load tap changers. Measurements are carried out using variable sensors including: optical fibers, vibration detectors, UHF antennas, gas sensors, thin film coils and capacitive sensors [4], [2], [3].

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TEMPERATURE MEASUREMENTS Power transformer can overheat during too heavy loading or because of a cooling system malfunction. Too high temperatures in transformer cause aging of insulation. A simple model of thermal aging is called Motsinger equation, which states that if the temperature is above 98 °C, every 6 °C rise of hot-spot temperature doubles the aging rate of the insulation. On-line temperature measurements along with thermal modeling are key factors in temperature based condition monitoring [5]. Temperature can be traditionally measured using a Pt100 thermocouple device. Generally these devices can be used to measure transformer oil temperatures but they are also used to measure temperatures in very difficult locations, such as in the magnetic core itself [6]. However one drawback of the Pt100 sensors is the induced electrical noise [5]. Thin film sensors Thin film sensors are an advanced technique to measure temperatures. Thin film sensors are constructed on metallic or non-metallic bases using several techniques. The sensor described here is constructed using a vacuum evaporation to form a miniature thin film thermocouple (type K). Thin film sensors are more reliable and accurate than traditional Pt100 thermocouple devices. Thickness of thin film sensors ranges from 12 to 50 nm, while Pt100 devices have thickness of 100 µm. Therefore the thin film sensors won’t create an air gap or change the flux inside the core, hence they are harmless to use in critical installation locations. Thin film sensors can be used in on-line temperature measurements [6]. Enhanced fiber optic temperature sensor Fiber optics can be also used in temperature measurements. Two traditional fiber optic measurement techniques exist; a point sensor and a distributed measuring system. They provide sufficient accuracy for on-line monitoring; +/- 1 °C for point sensors and +/-1 °C/m for distributed measurement system. Unfortunately these kinds of systems are very expensive and the long fibers are not robust enough. An enhanced fiber optic temperature sensor was constructed to overcome these drawbacks [5]. The new system uses a sensing probe integrated with a plastic cladding large-core (200 µm) optical fiber. Peeling a small portion of the cladding and using a reference liquid as a replacement forms the sensing element. When the temperature of the reference liquid changes, the refractive index is modulated and therefore the propagation regime of the fiber is modified. The temperature refractive index change of the reference liquid is known, making it possible to determine the temperature of the fluid where the probe is immersed. The temperature detection is based on analog to digital processing hardware to monitor the power output of the optical fiber. The resolution is 0,2 °C and accuracy is 0,5 °C for the measurement system [5].

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GAS ANALYSIS Power transformer gas-in-oil analysis (DGA) can be used for effective diagnostics and condition monitoring. Electrical and thermal stresses such as arching, partial discharges and overheating cause degradation of dielectric oil and solid dielectric cellulose materials. The degradation of insulation produces different gases. Important gases for fault detection include: H2, CO, CO2, CH4, C2H2, C2H4 and C2H6 [7]. Different degradation mechanisms generate different gases thus making it possible to determine the degrading part of the transformer [2]. On-line DGA Traditionally DGA is carried out using off-line measurements. However there are increasing availability of methods and sensors for on-line monitoring of dissolved gases. For example the Syprotec Hydran is widely used mainly for CO and H2 detection. It was developed in the late 1970’s. Recently, there have been better techniques for gas analysis using a membrane or vacuum extraction [8]. On-line measurements benefit from wide experience gained in laboratories over many decades. Semiconductor sensors, infrared sensors, combustible gas detector and gas chromatography are commercially available. Progress in process automation and microelectronics makes it possible to use more sophisticated measuring equipment, which can be used together with artificial neural networks (ANN) and fuzzy logic systems. These kind of methods applied to DGA can be used to reveal apparent fault conditions as well as hidden relationships between different fault types [7]. On-line gas phase monitoring On-line gas analysis can be carried out using infrared spectroscopy measurements. One application is to use a Clemet TM Fourier transform infrared spectroscope (FTIR) to measure the free gases in the inert gas blanket above the transformer oil. FTIR can detect all hydrocarbons and even Furans too. In temperatures above 120 °C the Furans are in gaseous state making it possible to detect them as gases. There has been discussion that measuring the free gas quantities will provide helpful information on insulation status more readily than DGA. If the continuous gas phase monitoring shows abnormal behaviour, more accurate diagnostic test should be conducted [9]. FURFURALDEHYDE MEASUREMENTS Concentration of furfuraldehyde (FFA) of the power transformers is usually measured during periodic inspections. Mostly used technique to measure FFA is high performance liquid chromatography (HPLC). Statistical survey shows that FFA concentration can vary from 0,1 to 10 ppm [10]. High performance liquid chromatography can be used for on-line FFA measurements but the systems are rather expensive [11].

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On-line FFA measurements using optical sensor An alternative method for FFA concentration determination is introduced. It is reported experimental by the author. The method uses toxic chemicals, which react with FFA to produce colored complex in solution. A linear correlation between the optical absorbance of the complex in solution and the concentration of FFA in the oil is established [10]. The method is based on solid porous 2 cm thick glass-like discs coated with aniline acetate layer of 1 mm. Several discs are immersed in oil and when FFA is present, the discs turn into pink colour in a few minutes. The sensor is made of a light source, monochromator, lens, two-branched light pipe (fiber), mirror and a detector. The light travels along the fiber and through the discs and then reflects back from the mirror and goes through the discs again and finally arrives to the detector. There are several discs to improve sensitivity and to shorten response time. The amount of absorbed optical wavelength of ~530 nm can be measured with detector. The system can detect 0,1 ppm FFA [10]. The figure 1 describes the behaviour of the normalized transmission as a function of wavelength and FFA ppm consentration.

Figure 1. The figure represents measurement of different FFA concentrations [10]. The minor drawbacks of this application are that the time taken for a disc to change color is temperature dependant and the current system components are little bulky. However, an on-line application is possible using for example few LEDs as the light source and more compact design [10]. MECHANICAL FAULT DETECTION USING FREQUENCY RESPONSE ANALYSIS Internal condition of the power transformers is difficult to determine without using non-intrusive tests. Internal condition may get worse due to winding movement. Usually winding movement is caused by short circuits and loss of winding clamping pressure. Internal damage may also occur in transit or during initial installation. Typically these types of faults alter the distributed capacitance or inductance of the windings. Frequency response analysis (FRA) can be used to

4

detect the internal mechanical faults but the results are affected by many factors leading to uncertain conclusion [12]. On-line low voltage impulse test method Low voltage impulse (LVI) also known as FRA is used to monitor possible winding movement on-line. The application is based on short low voltage impulse applied to the one transformer winding and then measuring the impulse again on another winding. A high voltage bushing capacitance tap was used as impulse injection and also for measurement. Impulse used in the measurements was a lightning impulse. Switching pulses were also considered, but they did not contain enough power in high frequencies [13]. Figure 2 shows a measured on-line voltage impulse.

Figure 2. The measured voltage impulse used in on-line FRA tests [13]. Using Fourier transform for the original input impulse and the measured output impulse, a transfer function can be calculated. By comparing the results of the transformer before initial installation and the results made later on, the possible winding movement can be detected [13]. This technique can be called as a difference technique. It is also possible to take signatures from each phase, which can be called as a signature technique. This technique shows the similarity of windings at the testing time and provides a reference to evaluate the faults or abnormalities in the future [12]. Additional methods for on-line FRA measurements are to use different frequency ranges to detect different type of faults. For example major faults such as winding displacement and grounding can be detected by low frequency spectrum range of 2 kHz, while minor faults like interturn faults and bulging of conductors can be identified by high frequency range of 2 MHz [12].

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TAN-δ ON-LINE MONITORING Measurement of the tan-δ of power transformer insulation can be used to determine the quality of the insulation as a standard test before initial installation. However, during in-service conditions such as interferences, limited time (periodic measurements) and difficult access to equipment makes the tan-δ measurements difficult to perform [14]. Field measurements needed the transformer to be disconnected because of the low testing voltage (10 – 12 kV) compared to the operating voltage of the transformer. Low levels of tan-δ are usually a sing of healthy insulation. Sudden increases in value in tan-δ over time are taken as a sign of insulation deterioration [2]. On-line continuous monitoring can overcome some of these drawbacks. The on-line method is based on a capacitor connected to a tap of HV equipment (e.g. a bushing). Together they form a voltage divider, which is used along with a reference voltage to calculate the tan-δ. The author reports applications for HV current transformers (CT) and bushings [14]. Therefore, a direct application for measuring a tan-δ of power transformer insulation is not reported. However, it is possible to monitor the tan-δ of a power transformer’s bushing, if the proper voltage divider tap is present. It is also possible to use a non-invasive capacitive divider as a sensor if the voltage tap is not present. The capacitive sensor is installed on the porcelain surface of the bushing next to the ground potential [15]. PARTIAL DISCHARGE MONITORING Power transformer breakdowns are most frequently preceded by partial discharges (PD). Monitoring the PD activity of the transformers can give valuable information about the possible breakdown [11]. Every partial discharge occurring inside the transformer generates an electrical pulse, a mechanical pulse and electromagnetic waves. These different types of pulses can be detected using various sensors and techniques. A very high frequency VHF PD detection uses narrowband measurements tuned to certain frequency with best sensitivity [2]. Also acoustical sensors can be used in frequency range of 100 – 300 kHz [11]. Acoustical sensors include for example microphones installed to the transformer hull. Also fiberglass rods immersed to the transformer oil can be used for acoustic measurements [23]. Ultra high frequencies (UHF) can be used to detect PD occurring inside the power transformers [16]. UHF PD detection Partial discharges occurring inside the transformer excite electromagnetic waves with resonance at frequencies 500 – 1500 MHz. Possible causes of PD include: a temporary overvoltage, weakness in the insulation introduced during manufacturing or due to various aging effects. Even if the PD pulses of an evolving fault are weak at first, PD will cause chemical decomposition and erosion of materials. UHF technique may use an UHF disc coupler that fits to the unused oil ports of the power transformers. Another approach is to use a dielectric window that is installed in the transformer hull [16], [17]. Different types of data handling layers are used in this on-line application. The system needed to be scalable and support new sensors, data sets and interpretation techniques as they become

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available. Therefore four data layers were constructed including data monitoring layer, interpretation layer, corroboration layer and information layer. The system will integrate also other monitoring technologies and data sources to provide more accurate and diverse analysis. PD sensor data can be analysed using these methods: time-energy mapping and clustering of PD data, time-frequency analysis combined with feature extraction and clustering and phase-resolved data representation [17]. New techniques for on-line PD measurements On-line PD measurements of HV equipment suffer from heavy noise caused by various origins. A severe noise enough can reduce the measurement sensitivity and accuracy of PD measurements. Both software and hardware (HW) approaches can be used to reduce the noise. New sensors, such as fiber optic and directional sensors together with multiple terminal measurements and better differential and balanced circuits are main points of hardware development. In software development different type of modern filters and noise gating technologies and advanced digital signal processing [18]. Couple of new sensors is introduced including a PD coupler board and multi-channel PD detector. The new PD coupler is not directly connected to the HV conductor or components. The new coupler consists of a sensing board, a high frequency transformer and amplifier. The sensing board is installed near of the HV conductor and the stray capacitance acts as a coupling capacitor. Also a new multi-channel PD detector uses advanced digital signal processing, directional sensing and noise gating techniques [18]. VIBRATION MEASUREMENTS There are two inner factors that can cause vibration in power transformers. First there is core vibration, which is caused by excitation by magnetostriction and excitation generated in the air gaps. Second there is winding vibration, which is generated by Lorenz force due to correlation of leakage flux and winding current. These vibrations are carried along the transformer oil to the transformer tank walls. Vibration signals seem to have strong relation with the conditions of the transformer core and windings. That’s why vibration measurements can provide helpful information in transformer condition monitoring [19]. A piezoelectric accelerometer is positioned at different locations on the transformer tank wall. The accelerometer was isolated from the tank wall using insulation to overcome heavy 50 Hz noise in the measured signal. Additionally the signal needed amplification and a charge amplifier input was connected to the accelerometer output. The measurements showed that vibration frequencies vary from 10 Hz to 2 kHz with amplitude of 0,5 µm – 50 µm. On-line measurements are conducted under load and no-load conditions. The spectrum of the signal is calculated for analysis. It is possible to find the spectrum of winding vibration by subtracting the no-load results from the loaded results. This is possible because the magnetic flux in the core is almost independent of the load [19]. Figures 3 and 4 represent the measuring system and some results. Different characteristics of the spectrum of the measured vibration of a power transformer in a good condition can be used as a reference during on-line monitoring. However, more study with

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database, vibration modeling and extracting the failure characteristic vector should be performed for proper condition analysis [19].

Figure 3. The diagram describes the measurement system [19].

Figure 4. The waveform on the right is the spectrum of the measured vibration [19]. MONITORING OF ON LOAD TAP CHANGERS Tap changers are the mechanical switching devices of the power transformer. On load tap changer (OLTC) cost is low compared to the power transformer, but its malfunction can destroy the complete transformer. Traditionally some temperature sensors were installed on OLTCs, but the sensors weren’t fast enough to detect the temperature changes [1]. On-line monitoring of OLTCs An international survey shows that OLTCs cause more failures and outages than any other component of a power transformer. OLTC possible failures include: motor malfunction, loss of power and flaw in controlling circuits may cause the tap change to stop before its completion. The mechanical parts can also wear out causing loss of synchronization between selector and diverter or jammed moving parts. In addition, the dielectric breakdown can occur due to failure of inter phase insulation or aging of the oil [20].

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The tap changer needs 5 – 6 seconds to finish its operation. During this time, different sensors are used for on-line monitoring. Vibrations caused by contact movements are at very high frequencies thus a high sampling rate is needed. The system consists of a 30 kHz accelerometer, clamp-on current transformer and pair of thermocouples. Monitoring of OLTCs is carried out using measurements on vibration of the contact movements, temperature of the insulation oil, voltage and current of the drive motor. The system is triggered by operation of the OLTC and above-mentioned measurements are conducted together with detection of the tap position. Figure 5 represents two different vibration waveforms. These measurements are collected to a database in order to do analysis. The database consists of signatures of a vibration fingerprint, drive motor current waveform envelope and the deviation between the temperatures of transformer main tank and OLTC tank [20].

Figure 5. This figure describes two different vibration waveforms of OLTCs. The above waveform represents a faulty condition and the bottom waveform a normal condition [21].

Figure 6. On the right there is a diagram of a mean value of the condition indicator after 2400 tap change operations [21].

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The on-line condition monitoring of OLTCs can be used to detect gradual deteriorating and sudden abnormalities as well. For example long-term measurements over 3 years showed a gradual drift in the mean value of the power transformer OLTC. The measurements included over 2400 tap change operations. Probabilities of rate of degradation can be thus determined using long-term tests together with short-term tests [21]. Figure 6 shows the waveform of the condition indicator value during the 3-year test. CONCLUSION The electrical equipment of the power network, particularly a power transformer, should function correctly without failures for many years. Various techniques for determining the condition of the transformer exist. Accurate enough long term continuous on-line monitoring together with more accurate and sophisticated off-line measurements can form a good technique for condition monitoring for power transformer. Good condition monitoring makes it possible to optimize the maintenance program thus minimizing the costs and maximizing the reliability. In this paper, various techniques were introduced. Some of them are still experimental and need more work in the future for reliable monitoring, while some are already commercially available. All techniques are promising and combining various measurement results, using for example fuzzy logic, neural network systems and different transformer models, an intelligent condition monitoring of power transformers can be established [3], [22]. However, the availability of many different type of measuring systems combined with insufficient knowledge of many aging mechanism and especially their interaction, makes an accurate and reliable condition monitoring a very challenging task to accomplish. REFERENCES [1] A. Basak, Condition monitoring of power transformers, Engineering science journal, pp.

41-46, 1999 [2] J. P. van Bolhuis, E. Gulski, and J. J. Smit, Monitoring and Diagnostic of Transformer Solid

Insulation, IEEE transactions on power supply delivery, vol. 17, no. 2, 2002 [3] T. Krieg, M. Napolitano, Techniques and experience in on-line transformer condition

monitoring and fault diagnosis in ElectraNet SA, Power System Technology, Proceedings, PowerCon, Vol. 2, 4-7 pp. 1019 - 1024, 2002

[4] T. Stirl, R. Skrzypek, C. Q. H. Ma, Practical experiences and benefits with on-line monitoring systems for power transformers, Electrical Machines and Systems. ICEMS 2003. Sixth International Conference on Volume 1, pp. 9-11, 2003

[5] G. Betta, A. Pietrosanto, A. Scaglione, An enhanced fiber-optic temperature sensor system for power transformer monitoring, Instrumentation and Measurement, IEEE Transactions on Volume 50, Issue 5, pp. 1138 – 1143, 2001

[6] F.J. Anayi, A. Basak, D.M. Rowe, Thin film sensors for flux and loss measurements, Condition Monitoring of Large Machines and Power Transformers, IEE Colloquium, Digest No: 086, pp. 3/1 - 3/4, 1997

[7] X.Q. Ding, H.Cai, On-line transformer winding's fault monitoring and condition assessment, Electrical Insulating Materials, Proc. ISEIM, pp.801 – 804, 2001

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[8] T. McGrail, A. Wilson, On-line gas sensors, Condition Monitoring of Large Machines and Power Transformers, IEE Colloquium, Digest No: 086pp. 1/1 – 1/4, 1997

[9] M. K. Pradhan, T. S. Ramu, Criteria for estimation of end of life of power and station transformers in service, Electrical Insulation and Dielectric Phenomena, CEIDP, Annual Report Conference, pp. 220 – 223, 2004

[10] R. Blue, D. G. Uttamchandani, A novel optical sensor for the measurement of furfuraldehyde in transformer oil, Instrumentation and Measurement, IEEE Transactions, Volume 47, Issue 4, pp. 964 – 966, 1998

[11] A. White, A transformer manufacturer's perspective of condition monitoring systems, HV Measurements, Condition Monitoring and Associated Database Handling Strategies, Ref. No: 448, IEE Colloquium, pp. 4/1 - 4/4, 1998

[12] S. Birlasekaran, F. Fetherston, Off/On-line condition monitoring technique for power transformers, Power Engineering Review, IEEE Volume 19, Issue 8, pp. 54 – 56, 1999

[13] M. Wang, A. J. Vandermaar, KD Srivastava, Condition monitoring of transformers in service by the low voltage impulse test method, High Voltage Engineering, Eleventh International Symposium, Conf. Publ. No: 467, Volume 1, pp. 45 - 48, 1999

[14] P. Vujović, R. K. Fricker, Development of an on-line continuous tan(δ) monitoring system, Electrical Insulation, IEEE International Symposium, pp. 50 – 53, 1994

[15] A. Setayeshmehr, A. Akbari, H. Borsi, E. Gockenbach, New sensors for on-line monitoring of power transformers’ bushings, Nordic Insulation Symposium, pp. 151-158, 2005

[16] J. Pearson, B. F. Hampton, M. D. Judd, B. Pryor, P. F. Coventry, Experience with advanced in-service condition monitoring techniques for GIS and transformers, HV Measurements, Condition Monitoring and Associated Database Handling Strategies, Ref. No. 448, IEE Colloquium, pp. 8/1 – 810, 1998

[17] M. D. Judd, S. D. J. McArthur, J. R. McDonald, O. Farish, Intelligent condition monitoring and asset management. Partial discharge monitoring for power transformers, Power Engineering Journal, Volume 16, Issue 6, pp. 297 – 304, 2002

[18] Q. Su, K. Sack, New techniques for on-line partial discharge measurements, Multi Topic Conference, IEEE INMIC, Technology for the 21st Century, Proceedings, IEEE International, pp. 49 – 53, 2001

[19] J. Schengchang, S. Ping, L. Yanming, X. Dake, C. Junling, The vibration measuring system for monitoring core and winding condition of power transformer, Electrical Insulating Materials, ISEIM, Proceedings, International Symposium, pp. 849 – 852, 2001

[20] P. Kang, D. Birtwhistle, J. Daley, D. McCulloch, Noninvasive on-line condition monitoring of on load tap changers, Power Engineering Society Winter Meeting, IEEE, Volume 3, pp. 2223 – 2228, 2000

[21] P. Kang, D. Birtwhistle, On-line condition monitoring of tap changers-field experience, Electricity Distribution Part 1, CIRED, 16th International Conference and Exhibition, IEE Conf. Publ No. 482), Volume 1, pp. 5, 2001

[22] O. Roizman, V. Davydov, Neuro-fuzzy computing for large power transformers monitoring and diagnostics, Fuzzy Information Processing Society, NAFIPS 18th International Conference of the North American, pp. 248 – 252, 1999

[23] D. Harris, M. Saravolac, Condition monitoring in power transformers, Condition Monitoring of Large Machines and Power Transformers, Digest No: 086, IEE Colloquium, pp. 7/1 - 7/3, 1997

11

TESTS AND DIAGNOSTICS OF INSULATING OIL

Kaisa Tahvanainen Lappeenranta University of Technology

Kaisa.Tahvanainen@lut.fi INTRODUCTION The importance of oil as an insulation material is significant in compositions, where warmth must be transported out or where it is important to impregnate the laminate insulation material. Oil is commonly used as insulating material in power and instrument transformers, switchgear installations, transformers, capacitors, bushings and cables. Experiences worldwide have shown that lack of attention to oil condition can lead to shorter operational lives of equipment. In addition to having good electrical, thermal and mechanical characteristics, insulation oil should endure the stress of service without deteriorating. Especially long term stress in high temperatures can alter the characteristics of insulation oil. This somewhat natural ageing of insulation oil can be fueled by electrical and chemical stress leading to poorer cooling conditions as well as insulation properties of oil and in the worst case in unplanned outages and failure of equipment. Changes in insulation oil can be analyzed in order to determine the oil condition and hence functioning of the equipment. In this paper, some of the most common insulating oil testing methods and principles are presented. Some of the oil testing techniques are expensive and require expert-knowledge. Testing is therefore done in the most important sites as the cost is small in comparison with that associated with insulation failure. This usually means on-line testing. Oil test performed on site can also be representative and can be performed by relatively unskilled staff. Many electricity distribution companies use laboratory tests for important sites as well as on-line monitoring of oil condition. INSULATING OILS Insulating oils have been used in oil filled electrical equipment as coolant and insulation since the 1900s. The purpose of insulation oil is to protect the solid parts of insulation structures (used in the construction of the equipment) from electric discharges, assist in quenching arcs or to dissipate heat generated in the equipment during use. The characteristics required for insulation oil depend on the equipment and circumstances in which the insulation is used. In transformers liquid insulation requires great dielectric strength and good conveyance of heat in order to ensure cooling. Insulation liquid should also have great resistivity, low loss factor and good tolerance for discharge. In cables and capacitors insulation liquid should possess low viscosity (impregnation) in addition to good tolerance for discharges. In addition to technical features the use of insulation liquids is influenced by environmental and life span facts. Lately, the research has focused on e.g. biodegradable vegetable-based oils, such as turnip rape oil. (Aro et al. 1996)

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Several different types of insulating oils have been produced and introduced into the market. These range from mineral to synthetic oils with different chemical and electrical properties for different applications. Mineral oil is the most common insulation liquid used due to its availability and affordability. Mineral oils consist of hydrocarbon composition of which the most common are paraffin, naphthene and aromatic oils. The main hydrocarbon composition of mineral oil and different impurities affect to what kind of features mineral oil possesses. Paraffin is the least likely to oxidize (antioxidants may be added to advance this), it is mainly used in breakers. In transformers paraffin precipitates easily, so naphthene is mainly used. Aromatic oils are most suited for cases, where good discharge tolerance is needed. The most common mineral oil in use is transformer oil (required characteristics are defined e.g. in standard IEC 60296). The electrical features and low viscosity make it a good insulation and refrigerating medium. Transformer oil boiling temperature is 250-300 ˚C and it is very fluid. It also oxidizes easily and is flammable (in liquid form the flashing point is however over 130 ˚C). The features of transformer oil in service are also influenced by moisture and impurities in addition to oxidation. (Aro et al. 1996) Synthetic insulation liquids include synthetic aromatic hydrocarbons, alkylbenzenes, esters, silicon oils and polybutans. Esters are used for refilling transformer oil or in combination of transformer oil. Silicon oil is synthetic oil, which is more environmentally friendly than transformer oil. It is also non-combustible. Synthetic oils are more expensive than mineral oils and they have poor thermal conductivity and discharge tolerance. Silicon oils cannot be used in breakers because arcs generate flammable gases. In addition silicon oils must be protected from moisture, because they absorb water. (Aro et al. 1996) In insulation constructions the combination of solid and liquid insulation is often used. By impregnating the solid insulation with liquid insulation, the insulation characteristics can be improved. By combining mineral oil and paper, insulation is created that has better electrical strength than either material alone. This kind of insulation is used in transformers, cables and capacitors. The weakness of this insulation combination is that both of the materials are sensitive to impurities, especially to moisture and oxidation. (Aro et al. 1996) INSULATION OIL TESTS In addition to having good electrical, thermal and mechanical characteristics, insulation structures should endure the stress of service without deteriorating (Aro et al. 1996). Prolonged bulk oil temperatures of greater than about 75°C usually involve spot temperatures of over 98°C. Further increases rapidly increase degradation of oil impregnated paper and pressboards. Ageing of these cellulose-based materials is also increased by higher moisture and oxygen contents. Additionally such high temperatures increase the rate of oxidation of the oil, producing acids, moisture and sludge which impair both cooling properties and dielectric strength. Water increases the rate of oil oxidation and effectively self-catalyses the reaction. All of these changes increase the possibility of electrical breakdown. Chemical and physical analysis gives information on serviceability of the oil as both an insulator and coolant. (Myers 1998) Oil testing can be split into two categories, tests that are performed on site and test that are performed in an oil laboratory (table 1). On-site tests are intended to determine the oil quality and

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acceptance of new oil at the point of delivery. There are limited tests that can be performed on site and they are relatively simple and low cost. Furthermore, non-specialist personnel usually can carry out these tests. Laboratory tests are carried out in a controlled environment in the laboratory for quality of the supplied unused oil, condition of in service oil and plant condition monitoring. (Pahlavanpour et al. 1999)

Table 1. Location and type of the test conducted on insulation oil. (Pahlavanpour et al. 1999)

Location of test Type of test Field test Moisture

Breakdown voltage Color and appearance

Laboratory test Acidity Resistivity Breakdown voltage Moisture Dielectric dissipation factor Interfacial tension Flash point Dissolved gas analysis Furan analysis

Off-line techniques can only be carried out during outages. They are only applicable to large or strategically important sites. Post fault forensic tests also include paper analysis and metallurgical tests. To use the information from these tests as a fault diagnostic or an ongoing monitoring programme, it is necessary to compare results with previous datum levels. These tests are generally sensitive and specific and are not expensive in the context of fault detection for such plant. A multi-parameter approach is essential. On-line techniques may be by discrete tests or can be applied continuously and avoid the need for outages. Temperature is usually recorded for large plant, but for routine detection of abnormal conditions while maintaining output, analysis of insulating oil provides a cheap but powerful tool to evaluate the condition of any oil-insulated plant, be it power or instrument transformers, bushings, cables or switches. (Myers 1998) In general, the optimum interval for sampling and testing of oil will depend on type of equipment in operation and power, action and service conditions of the equipment. The duty experienced by insulating oil in transformers and selectors is different from that experienced in circuit breakers and divertors. This may lead to different changes in chemical characteristics of the oil and a different rate of oil deterioration. A check interval can be every 1-4 years. Economical factors and reliability requirements have to be compromised. Details of the oil sampling technique are outlined in IEC 60475. Frequency of oil sampling and testing is given in IEC 60422. (Pahlavanpour et al. 1999)

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Dissolved Gas in Oil Analysis Dissolved gas analysis (DGA) is widely accepted as the most reliable tool for the earliest detection of inception faults in electrical equipments using insulating oil. Gas-in-oil analysis by gas chromatography (for details, see Annex 1) has proven to be predictive and valuable for some of the problems, such as arcing, corona, overheated oil, and cellulose degradation. When insulating oils and cellulose materials in reactive equipment are subjected to higher than normal electrical or thermal stresses, they decompose to produce certain combustible gases referred to as fault gases. For incipient fault conditions (i.e. slowly evolving fault), the gases generated will be dissolved into the oil long before any free gas is accumulated in the gas relay. Thus by analyzing oil sample for dissolved gas content it is possible to asses the condition of the equipment and detecting faults at an early stage. If a fault is indicated, the type of fault can be predicted using various analysis methods. Table 2 shows the concentrations for gases to determine whether there is any problem and whether there is sufficient generation of each gases for the ratio analysis to be applicable. (Ward 2003)

Table 2. Concentration of dissolve gas (Saha 2003).

Key gas Concentrations (ppm) H2 100

CH4 120 CO 350

C2H2 35 C2H4 50 C2H6 65

Several DGA tests should he taken over a period of time, in order to determine the rate of increase of the fault gases, and therefore the rate of advancement of the fault. The assumption is made that the change in the rate should not exceed 10 percent per month for gases, the concentration of which exceeds the typical concentration value. (Arakelian 2002) The most important part of DGA is interpretation of the results which can vary from simple use of key gases to suggest a type of fault, to sophisticated computerized calculations of gas ratios, rates of increase, equilibrium between free and dissolved gases and predicted times to Buchholz alarm operation. Such systems, used by experienced analysts, can decrease effort on routine samples where no significant changes occur, allowing more time to examine the subtle changes that can give early warning of incipient faults. Trend analysis is of paramount importance, as residual effects from previous faults and multiple types of overheating can seriously distort interpretation of results from a one-off sample. (Myers 1998) Interpretation of DGA results is often complex and should always be done with care. Doernenberg, Roger’s and Duval’s triangle methods are the most commonly used in gas-in-oil diagnostics in addition to IEC 60599. The key gas method identities the key gas for each type of fault and uses the percent of this gas to diagnose the fault. Key gases formed by degradation of oil and paper insulation are hydrogen (H2), methane (CH4), ethane (C2H6), ethylene (C2H4), acetylene (C2H2), carbon monoxide (CO)

4

and oxygen (02). Except for carbon monoxide and oxygen, all these gases are formed from the degradation of the oil itself. Acetylene is mainly associated with arcing, where temperatures reach several thousand degrees, ethylene with hot spots between 150°C and 1000°C and hydrogen with the partial discharges. Gas type and amounts are determined by where the fault occurs in the transformer and the severity and energy of the event. The IEC-standard 60599 is a guide describing how the concentrations of dissolved gases or free gases may be interpreted to diagnose the conditions of oil-filled electrical equipment in service and suggest further action. In addition to threshold values given in the standard, it is suggested that empirical values characteristic to the device type could be used. According to the recommendations of IEC 60599, the existing method for the interpretation of gas analysis is based on the ratio of concentration of CH4/H2, C2H2/C2H4, and C2H4/C2H6 to evaluate the defect. These ratios are to be used when the concentration of at least one of the gases exceeds the limiting concentration for normal equipment. (Ward 2003)

Table 3. Examples of the interpretations of dissolved gas analysis (Aro et al. 2003)

Ratios of characteristics gases Fault type

42

22

HCHC

2

4

HCH

62

42

HCHC

Partial discharges insignificant <0,1 <0,2 discharges of low energy density >1 0,1-0,5 >1 discharges of high energy density 0,6-2,5 0,1-1 >2 Hot spots T< 300 ˚C insignificant insignificant <1 Hot spots 300 < T < 700 ˚C <0,1 >1 1-4 Hot spots T< 700 ˚C <0,2 >1 >4

Furan analysis Electrical aging of paper, which is an integral part of oil insulation (oil-barrier insulation, paper-oil insulation), can lead to the formation of light gases. The thermal influence on paper initiates dehydration processes, resulting in the formation of water and compounds related to furans. The presence of oxygen and increased temperature initiate oxidizing reactions in the cellulose insulation. (Arakelian 2002) The five most prevalent derivatives of furan that arise from the degradation of the cellulose and that are soluble in the oil are 2-Furaldehyde, Furfuryl alcohol, 2-Acetylfuran, 5-Methyl-2-furaldehyde and 5-Hydroxymethyl-2-furaldehyde. A sample of the oil is extracted with either another liquid such as acetonitrile or with a solid phase extraction device. The extract is then analyzed using liquid chromatography. The five compounds mentioned above are separated on an appropriate column and each is detected by use of an ultraviolet detector that is adjusted automatically to the appropriate wavelength for each of the five components. Calibration solutions are made up for each of the components to be analyzed and these are used to standardize the instrument responses. From the data on the standard solutions, the extraction efficiencies for each component can be calculated and corrections can be made accordingly. The results are usually reported in terms of parts per billion (ppb). (NTT)

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Moisture/Water content During the service life of an oil-filled equipment, the moisture content may increase by breathing damp air, natural ageing of the cellulose insulation, oil oxidation, condensation or by accidental means and the water content of the paper may rise to five or even six percent. The presence of moisture increases the ageing rate of both the oil and the paper. Insulating paper with a one percent moisture content ages ten times faster than one with only 0.1 %. Water is a polar liquid and is attracted to areas of strong electrical field. Water-soluble acids produced by oxidation of the oil act as a catalyst for almost all reactions and will combine with water or oil to assist or promote corrosion to exposed metal parts in the equipment. The cellulose has a greater affinity for water than oil and so water will replace the oil in oil-impregnated cellulose. Presence of water in the oil will reduce the electrical strength of the oil and may shorten life of the insulation system and lead to early transformer failure. Like other oil properties, the moisture content should be monitored regularly. (Pahlavanpour & Roberts 1998) A number of techniques have been investigated over the years to measure the quantity of moisture in a dielectric fluid, but the only method which has stood the test of time is that developed by Karl Fischer in the early 1930's. The method is outlined in IEC 60814; Insulating liquids – Oil-impregnated paper and pressboard – Determination of water by automatic coulometric Karl Fischer titration. Titration is a chemical analysis that determines the content of a substance, such as water, by adding a reagent of known concentration in carefully measured amounts until a chemical reaction is complete. There are two types of Karl Fischer titrators: volumetric and coulometric titrators. The main difference between the two is that with the volumetric method, the titrant is added directly to the sample by a burette. Conversely, with the coulometric method, the titrant is generated electrochemically in the titration cell. The coulometric method measures water levels much lower than the volumetric method. Measurements by coulometric Karl Fischer moisture meters, is quick and can be carried out by relatively unskilled staff. It is capable of measuring moisture contents down to 1 ppm or 0,0001% in the oil and can perform field analysis. The most obvious direct benefit of a portable moisture meter is the elimination of the possibility of further contamination that might occur while a sample is being transferred to a laboratory for analysis. (Pahlavanpour & Roberts 1998, Poynter & Barrios 1994) Estimates of moisture content of the cellulose can be made by relating water content of the oil in ppm to the % concentration of water in the cellulosic insulation. However, this requires knowledge of the moisture equilibrium data for the oil in question together with its normal temperature and moisture content. Also, for a long time, water determination has not been a problem for gas chromatography. The water extraction from transformer oil occurs simultaneously with the extraction of gases. The maximum allowable water content of oil in service depends on the transformer voltage, recommended value of 30 ppm at delivery is given in IEC 60296 for new oil. (Pahlavanpour & Roberts 1998, Poynter & Barrios 1994)

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Acidity/Neutralization Number (NN) The acidity of an oil sample is related to the deterioration of the oil. New oils contain practically no acids if properly refined. The acidity test measures the content of acids formed through oxidation. The oxidation products polymerize to form sludge which then precipitates out. Acids react with metals on the surfaces inside the tank and form metallic soaps, another form of sludge. The presence of these acidic materials can be quantitatively determined by a procedure called titration. The amount of a standardized base that is needed to neutralize the acidic materials present in a known quantity of an oil sample is determined. The result referred to as the acid number (formerly referred to as neutralization number) equals the milligrams of KOH (potassium hydroxide) required to neutralize the acid contained in 1 gram of oil. The titration procedure can be done either volumetrically or gravimetrically and the end point can be determined either colorimetrically or potentiometrically (IEC 62021-1). With old oils, the colourimetric determination is sometimes difficult because of the dark colour of the oil (Myers 1998). The maximum neutralization value given by the IEC 60296 is 0,03 mgKOH/g. (NNT) Interfacial Tension The interfacial tension (IFT) test is employed as an indication of the sludging characteristics of oil (soluble polar contaminants and products of deterioration). In this procedure the surface tension of the oil is measured against that of water, which is highly polar. The more nearly the two liquids are alike in their polarity the lower the value of the surface tension between them. Thus the higher the concentration of hydrophilic materials in the insulating fluid, the lower will be the interfacial tension of the oil measured against water. The attraction between the water molecules at the interface is influenced by the presence of polar molecules in the oil in such a way that the presence of more polar compounds causes lower IFT. The test measures the concentration of polar molecules in suspension and in solution in the oil and thus gives an accurate measurement of dissolved sludge precursors in the oil long before any sludge is precipitated. There are several methods that can be used to measure the interfacial tension of oil against water. One method measures the size of a drop of water that is formed below the surface of the oil, however, if more accurate values are needed it is recommended that the Nouy ring method is used. The method involves placing a clean, platinum wire ring on the surface of the oil, where the force required to pull the ring away from the surface is measured. The method uses a tensiometer and a platinum ring. The ring is lowered into a beaker of water and oil. It is then brought up to the water-oil interface where the actual measurement takes place. The force required to pull the ring through the interface is measured by the tensiometer and considered to be the interfacial tension of the oil. The value for mineral oil varies from 24 to 40 dynes/cm (IEEE C57.106-1991). (NNT)

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Dielectric dissipation factor (tan δ) The power factor of insulating oil is the cosine of the phase angle between a sinusoidal potential applied to the oil and the resulting current. This can be measured for example using Schering bridge. For insulating oils the value for this characteristic is called the power factor, loss tangent or dissipation factor and is expressed at a specified temperature. Power factor indicates the dielectric loss of an oil; thus the dielectric heating. Oxidation and contamination of oil can cause dissipation factor of an oil to rise, so determination of this property may provide useful information about used electrical insulating oil. Since these values vary with temperatures, comparisons must always be made at the same temperature. Test methods are outlined in IEC 61620 (Insulating liquids – Determination of the dielectric dissipation factor by measurement of the conductance and capacitance – Test method) and IEC 60247 (Measurement of relative permittivity, dielectric dissipation factor and d.c. resistivity of insulating liquids). The maximum value for dissipation factor is determined in IEC 60296 to be 0,005 at 90 ˚C (50 Hz). (Aro et al. 1996) Electrical Breakdown Strength The breakdown voltage is indicative of the amount of contaminant (usually moisture) in the oil. The effect of moisture in insulation oil increases when there are impurities present in oil. Also the oil temperature affect to the breakdown strength. Testing electrical breakdown strength begins by immersing two electrodes in a sample of the oil, then applying an AC voltage across the electrodes. The voltage is then increased in a specified manner until electrical breakdown occurs. The various tests used differ in electrode spacing and shape, rate of increase of voltage (and durance of test in DC voltage) and thus give different breakdown values for the same oil. Electrical breakdown strength for new, clean transformer oil in AC measurements is over 60 kV/2,5 mm (effective value) and the breakdown strength is independent of the oil brand. Testing is standardized in IEC 60156. Oil is not necessarily in good condition even when the dielectric strength is adequate because this tells nothing about the presence of acids and sludge. IEC 60296 determines minimum value for AC breakdown voltage at delivery to be 30 kV and 50 kV after treatment. (NNT, Aro et al. 1996) Resistivity The resistivity of electrical insulating oil is a measure of the resistance to DC current flow between conductors. The resistivity of mineral insulating oil is naturally high but, as with loss tangent, is very sensitive to the presence of even minute amounts of suspended water, free ions or ion forming materials such as acidic oxidation products or polar contaminants. (Aro et al. 1996)

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Flash point Flash point is an indication of the combustibility of the vapors of a mineral oil and is defined as the lowest temperature at which the vapor of oil can be ignited under specified conditions. The flash point is considered to be the lowest temperature at which the oil vapors will ignite, but not sustain a flame. Impurities in oil lower the flash point. Usual method for flash point determination is Pensky Martens closed cup flash point test. IEC 60296 determines the minimum value for flash point to be 140 ˚C for higher viscosity mineral oil and 130 ˚C for lower viscosity mineral oil. Method used is ISO 2719. Analysis of antioxidant Oxidation inhibitors in mineral oils readily react with oxygen at elevated temperatures to first form hydroperoxides, then organic acids. These compounds lead to viscosity increase, formation of sludge, discoloration, acidic odor and corrosion of metal parts. Oxidation resistance may be due to natural inhibitors or commercial additives. Four types of oxidation inhibitor additives are zinc dithiophosphates, aromatic amines, alkyl sulfides and hindered phenols. Metal surfaces and soluble metal salts, especially copper, usually promote oxidation. Therefore, another approach to inhibiting oxidation is to reduce the catalysis by deactivating the metal surfaces. The effectiveness of the anti-oxidants in delaying oil oxidation can be measured by laboratory tests known generally as oxidation stability tests. Oxidation stability is measured in accelerated tests at high temperature, in the presence of excess oxygen, catalysts and possibly water. Results are expressed as the time required to reach a predetermined level of oxidation. Criteria can be a darkening color, the amount of sludge, gum, acids and the amount of oxygen consumed and in some cases by the depletion of the anti-oxidant chemical compound itself. The maximum value given by the IEC 60296 for sludge is 0,10 % by mass or 0,40 mgKOH/g for neutralization value. Method used is IEC 1125. (Godfrey & Herguth 1995) Viscosity Viscosity is the resistance of oil to flow under specified conditions. The viscosity of oil used as a coolant influences heat transfer rates and consequently the temperature rise of an apparatus. Low viscosity ensures that oil flows well in particularly in low temperatures and helps quenching arcs. The viscosity of oil also influences the speed of moving parts in tap changers and circuit breakers. The IEC 61868 specifies a procedure for the determination of the kinematic viscosity of mineral insulating oils, both transparent and opaque, at very low temperatures, after a cold soaking period of at least 20 h, by measuring the time for a volume of liquid to flow under gravity through a calibrated glass capillary viscometer. The number of seconds the oil takes to flow through the calibrated region is measured. The oil's viscosity in cSt is the flow time in seconds multiplied by the apparatus constant. It is particularly suitable for the measurement of the kinematic viscosity of liquids for use in cold climates, at very low temperatures (–40 °C) or at temperatures between the cloud and pour-point temperatures (typically –20 °C) where some liquids may develop

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unexpectedly high viscosities under cold soak conditions. IEC 60296 determines the minimum value for viscosity to be 16,5 cSt at 20 ˚C and 800 cSt at -15 ˚C for higher viscosity mineral oil. For lower viscosity mineral oil the values are 11,0 cSt and 1800 cSt. (Godfrey & Herguth 1995) Color and appearance Mineral oil fresh from the refinery is essentially colorless. As the sample ages over time or is subjected to severe conditions such as local hot spots or arcing the sample will become darker in color. The clarity of a fresh virgin sample of oil should be sparkling with no indication of cloudiness, sludge, or particulate matter. The clarity of an oil sample is determined by observation of the sample when illuminated by a narrow focused beam of light. The color of a sample is determined by direct comparison to a set of color standards. Also the assessment of the smell of oil from different parts of a transformer can be a valuable first indicator of the source and type of fault (Myers 1998). It should be pointed out that the color of the oil by itself should never be used to indicate the quality of the oil, rather it can be used to determine whether more definitive tests should be done. (NNT) The insulation oil condition can also be determined by its fibre and particulate content. A simple count of visible fibres can be made using a crossed Polaroid viewing system with results usually reported in terms of small (<2 mm), medium (2 - 5 mm) and large fibres. The range of 2 - 5 mm includes most cellulose fibres derived from the paper insulation whereas larger fibres are usually contaminants introduced either during maintenance or sampling. These results, in conjunction with water content, can give an indication of the cause of poor electrical strength. Accurate fibre and particulate counts require carefully controlled filtration in clean conditions followed by microscopic examination and are only needed for very high quality insulation is required. (Myers 1998) CONCLUSIONS Practical diagnostics of oil filled equipment are executed in a standard manner, see picture 1. Sample of oil is taken regularly from the equipment to enable the early determination of developing defects. If all values remain below the limiting values, the condition of the insulation is considered to be satisfactory. For abnormal values, the analysis is repeated to confirm the results, and to calculate the rate at which the defect is developing. If the abnormal results are not confirmed, or the dynamics of the development of the defect are absent, normal operation can be assumed, especially if additional tests to determine the electrical, physical, physico-chemical, and chemical characteristics of the oil are normal. On confirmation of a problem, the type of defect—thermal or electrical—and its severity are determined, and a decision is made on further checking by means of monitoring or frequent gas chromatographic analysis. The recommendation for refurbishment, repair, or replacement is made on the basis of the data accumulated (IEC 60422, Supervision and maintenance guide for mineral insulating oils in electrical equipment) (Arakelian 2002)

10

Scheduled Periodic Inspection of OFEE in Service

We are dealing or talking about oil diagnostics only when information on the owner, oil sampling, oil testing results, technical and operational data and history of oil maintenance actions, are gathered periodically with expert opinion about suitability of the tested oil filling together with guidelines for any needed corrective actions (filtering, adding inhibitor, exchange or reclaiming). For the optimal oil diagnostics, the expert opinion should take into account also the owners strategic lifetime of equipment and his maintenance and investment strategy, as critical values of specific test results (degree of degradation), at which some actions should be done, depend on these parameters. Having all cited information the diagnostic expert should also advise the techno-financial optimal frequency and type of oil testing. The highly experienced expert having enough information can reduce expenses for equipment supervision, maintenance and refurbishment. (Gradnik 2002)

Electrical GC-Analysis of Oils from OFEE for the Concentrations of Dissolved Gases

and Water

Thermovision Control Measurements

No Deviations Present Deviation from Normal,

Assume Defect Present

Repeat GC –Analysis for

Concentrations of Gases and Water

Additional Selective Measurements:

1. Tanδ, UBreakdown, ρv 2. Furfural,

Antioxidant 3. d20, n20, σ20 4. Acidity

Establish or Confirm Defect Present and Determine its

Rate of Development

Continue in Service

Decision on Type of Monitoring

Monitoring

Decision on Periodic GC-Control

The Decision on Scale, Type and Expediency of Repair

Repair

Possible Problems

Problems No Problems

Continue in Service

Picture 1. Ideology and tactics of oil-filled electronic equipment (OFEE) diagnostic check (Arakelian 2002).

11

REFERENCES (Arakelian 2002) Arakelian, V.G, 2002. Effective Diagnostics for Oil-Filled

Equipment. IEEE Electrical Insulation Magazine, November/December 2002 – Vol. 18, No. 6.

(Aro et al. 1996) Aro, M., Elovaara, J., Karttunen, M., Nousiainen, K., Palva, V.,

1996. Suurjännitetekniikka. Otatieto 568. Jyväskylä 2003. ISBN 951-672-320-9.

(Barnes 2002) Barnes, M. 2002. Gas Chromatography: The Modern Analytical

Tool. Practicing Oil Analysis Magazine. Available: www.practicingoilanalysis.com/article_detail.asp?articleid=352&relatedbookgroup=OilAnalysis

(Gradnik 2002) Gradnik, M.K., 2002. Physical-Chemical Oil Tests, Monitoring and

Diagnostics of Oil-Filled Transformers. Proceedings of 14th International Conference on Dielectric Liquids, Austria.

(Godfrey & Herguth 1995) Godfrey D. & Herguth W. R. 1994. Physical and Chemical

Properties of Mineral Oil That Affect Lubrication. Herguth Laboratories. Available: www.herguth.com/technical/PHYSICAL.HTM

(Myers 1998) Myers, C., 1998. Transformers – Conditioning Monitoring by Oil

Analysis Large or Small; Contentment or Catastrophe. Power Station Maintenance: Profitability Through Reliability, Conference Publication No. 452.

(NNT) Northern Technology & Testing. Available:

www.nttworldwide.com (Pahlavanpour et al. 1999) Pahlavanpour, B., Wilson, G., Heywood, R. 1999. Insulating Oil in

Service: Is It Fit for Purpose? The Institution of Electrical Engneers.

(Pahlavanpour & Roberts 1998)

Pahlavanpour B., Roberts I. A., 1998, Transformer Oil Condition Monitoring. The Institution of Electrical Engneers.

(Poynter & Barrios 1994) Poynter W.G & Barrios R.J. 1994. Coulometric Karl Fischer

titration simplifies water content testing. Oil & Gas Journal. Available : www.kam.com/techcenter-karlfischer.htm

(Saha 2003) Saha, T. 2003. Review of Modern Diagnostic Techniques for

Assessing Insulation Condition in Aged Transformers). IEEE Transactions on Dielectrics and Electrical Insulation.

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(Ward 2003) Ward S.A., 2003. Evaluating Transformer Condition Using DGA

Oil Analysis. 2003 Annual Report Conference in Electrical Insulation and Dielectric Phenomena.

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Annex 1

Gas-Liquid Chromatography In gas-liquid chromatography, it is the interaction between the gaseous sample (the mobile phase) and a standard liquid (the stationary phase), which causes the separation of different molecular constituents. The stationary phase is either a polar or nonpolar liquid, which, in the case of capillary column, coats the inside of the column, or is impregnated onto an inert solid that is then packed into the GC column.

Figure 2. Gas Chromatography Instrument.

A schematic layout of a GC instrument is shown in figure 2. The basic components are an inert carrier gas, most commonly helium, nitrogen or hydrogen, a GC column packed or coated with an appropriate stationary phase, an oven that allows for precise temperature control of the column and some type of detector capable of detecting the sample as it exits or elutes from the column. Gas-liquid chromatography works because the molecules in the samples are carried along the column in the carrier gas, but partition between the gas phase and the liquid phase. Because this partitioning is critically dependent on the solubility of the sample in the liquid phase, different molecular species travel along the column and elute at different times. Those molecules that have a greater solubility in the liquid phase take longer to elute and thus are measured at a longer interval. Solubility is dependent on the physical and chemical properties of the solute; therefore, separation between different components of the sample occurs based on molecular properties such as relative polarity (like ethylene glycol versus base oil) and boiling point (like, fuel versus diesel engine base oil). For example, using a polar stationary phase, with a mixture of polar and nonpolar compounds will generally result in longer elution times for the polar compounds, because they will have greater solubility in the polar stationary phase. (Barnes 2002)

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1

GAS DIAGNOSTICS IN TRANSFORMER CONDITION MONITORING

Pauliina Salovaara Tampere University of Technology, Institute of Power Engineering

pauliina.salovaara@tut.fi ABSTRACT Transformers are vital components in both the transmission and distribution of electrical power. The early detection of incipient faults in transformers reduces costly unplanned outages. The most sensitive and reliable technique for evaluating the health of oil filled electrical equipment is dissolved gas analysis (DGA). Insulating oils under abnormal electrical or thermal stresses break down to liberate small quantities of gases. The qualitative composition of the breakdown gases is dependent upon the type of fault. By means of dissolved gas analysis (DGA), it is possible to distinguish faults such as partial discharge (corona), overheating (pyrolysis) and arcing in a great variety of oil-filled equipment. Information from the analysis of gasses dissolved in insulating oils is valuable in a preventative maintenance program. A number of samples must be taken over a period of time for developing trends. Data from DGA can: Provide advance warning of developing faults, provide a means for conveniently scheduling repairs and monitor the rate of fault development. INTODUCTION Monitoring and maintenance of mineral-oil-filled power transformers are of critical importance in power systems. Failure of a power transformer may interrupt the power supply and result in loss of profits. Therefore, it is of great importance to detect incipient failures in power transformers as early as possible, so that we can switch them off safely and improve the reliability of power systems. If a long in-service transformer is subjected to higher than normal electrical and thermal stresses, it may generate by-product gases due to the incipient failures. Dissolved gas analysis (DGA) is a common practice for incipient fault diagnosis of power transformers and widely accepted as the most reliable tool for the earliest detection of inception faults in transformers and other electrical equipments using insulating oil. [7,8] The utility tests and periodically samples the insulation oil of transformers to obtain the constituent gases in the oil, which are formed due to breakdown of the insulating materials inside. Gas-in-oil analysis by gas chromatography has proven to be predictive and valuable some of the problems, which could progress to catastrophic failures in transformers. Problems that can be detected are: arcing, corona, and both overheated oil and cellulose degradation. These problems result in gas production as they start to develop and gas production increases with increasing severity of the problem. The energy dissipation is the least in corona, medium in overheating, and highest in arcing. According to the IEC Standard (Publication 567), nine dissolved gases can be determined from a DGA test (i.e., hydrogen (H2), oxygen (O2), nitrogen (N2) methane (CH4), ethane (C2H6), ethylene (C2H4), acetylene (C2H2), carbon monoxide (CO), and carbon dioxide (CO2)). Therefore, if we can relate the future gas content of transformers with the faults, then forecasting fault conditions for power transformers will be easily done. Future prediction of fault conditions is the most import information for maintenance engineering group to avoid system outages. In the past, various fault diagnosis

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techniques have been proposed, including the conventional key gas method, gas ratio method, expert systems, neural networks (NN), and fuzzy logic approaches. Recently, the combinations of fuzzy logic and artificial intelligence (AI) have given promising results in the fault analysis. [7,8] METHODS OF GAS DETECTION Three different methods of gas detection will be discussed and their advantages and disadvantages will be compared. The first method is the one that determines the total combustible gases (TCG) that are present in the gas above the oil. The major advantage of the TCG method compared to the others that will be covered is that it is fast and applicable to use in the field. In fact it can be used to continuously monitor a unit. However, there are a number of disadvantages to the TCG method. Although it detects the combustible fault gases (hydrogen, carbon monoxide, methane, ethane, ethylene, and acetylene), it does not detect the non-combustible ones (carbon dioxide, nitrogen, and oxygen). This method is only applicable to those units that have a gas blanket and not to the completely oil-filled units of the conservator type. Since most faults occur under the surface of the oil, the gases must first saturate the oil and diffuse to the surface before accumulating in the gas blanket above the oil. These processes take time, which delays the early detection of the fault. The major disadvantage of the TCG method is that it gives only a single value for the percentage of combustible gases but does not identify which gases are actually present. It is this latter information that is most useful in determining the type of fault that has occurred. [1] The second method for the detection of fault gases is the gas blanket analysis in which a sample of the gas in the space above the oil is analyzed for its composition. This method detects all of the individual components; however, it is also not applicable to the oil-filled conservator type units and it also suffers from the disadvantage that the gases must first diffuse into the gas blanket. In addition, this method is not at present best done in the field. A properly equipped laboratory is preferred for the required separation, identification, and quantitative determination of these gases at the part per million level. [1] The third and most informative method for the detection of fault gases is the dissolved gas analysis (DGA) technique. In this method a sample of the oil is taken from the unit and the dissolved gases are extracted. Then the extracted gases are separated, identified, and quantitatively determined. At present this entire technique is best done in the laboratory since it requires precision operations. Since this method uses an oil sample it is applicable to all type units and like the gas blanket method it detects all the individual components. The main advantage of the DGA technique is that it detects the gases in the oil phase giving the earliest possible detection of an incipient fault. This advantage alone outweighs any disadvantages of this technique. [1] FAULT GASES Insulating materials within transformers and related equipment break down to liberate gases within the unit. The distribution of these gases can be related to the type of electrical fault and the rate of gas generation can indicate the severity of the fault. The identity of the gases being generated by a particular unit can be very useful information in any preventative maintenance program. The causes of fault gases can be divided into three categories; corona or partial

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discharge, pyrolysis or thermal heating, and arcing. These three categories differ mainly in the intensity of energy that is dissipated per unit time per unit volume by the fault. The most severe intensity of energy dissipation occurs with arcing, less with heating, and least with corona. [1] Mineral insulating oils are made of a blend of different hydrocarbon molecules containing CH3, CH2 and CH chemical groups linked together by carbon-carbon molecular bonds. Some of the C-H and C-C bonds may be broken as a result of electrical and thermal faults, with the formation of small unstable fragments, in radical or ionic form, which recombine rapidly through a complex reaction, into gas molecules such as hydrogen, methane, ethane, ethylene, acetylene, C3, and C4 hydrocarbon gases, as well as solid particles of carbon and hydrocarbon polymers (X-wax), are other possible recombination products. [9] Fault gases that can be found within a unit are listed in the following three groups:

1. Hydrocarbons and hydrogen: methane (CH4), ethane (C2H6), ethylene (C2H4), acetylene (C2H2), hydrogen (H2) 2. Carbon oxides: carbon monoxide (CO), carbon dioxide (CO2) 3. Non-fault gases: nitrogen (N2), oxygen (O2)

Except for carbon monoxide, nitrogen and oxygen, all these gases are formed from the degradation of the oil itself. Carbon monoxide, carbon dioxide (CO2), and oxygen are formed from degradation of cellulose (paper) insulation. [8] Faults can be identified based on the formed fault gases. Below is a list of fault and the main fault gases [10]

o Arcing: Large amounts of hydrogen and acetylene are produced, with minor quantities of methane and ethylene. Key Gas: Acetylene

o Corona: Low-energy electrical discharges produce hydrogen and methane, with small quantities of ethane and ethylene. Key Gas: Hydrogen

o Overheated Oil: Decomposition products include ethylene and methane, together with smaller quantities of hydrogen and ethane. Key Gas: Ethylene

o Overheated Cellulose: Large quantities of carbon dioxide and carbon monoxide are evolved. Key Gas: Carbon monoxide

Figures 1, 2, 3, and 4 illustrate the chemical processes occurring with corona, pyrolysis, and arcing in oil and pyrolysis of cellulose respectively. Typical fault gas distributions are also shown. [1]

4

Figure 1. Corona in Oil

H2 88 % C02 1 % C0 1 % CH4 6 % C2H6 1 % C2H4 0.1 % C2H2 0.2 %

Figure 2. Pyrolysis in Oil

H2 16 % C02 trace C0 trace CH4 16 % C2H6 6 % C2H4 41 % C2H2 trace

Figure 3. Arcing in Oil

H2 39 % C02 2 % C0 4 % CH4 10 % C2H4 6 % C2H2 35 %

Figure 4. Pyrolysis of Cellulose

H2 9 % C02 25 % C0 50 % CH4 8 % C2H4 4 % C2H2 0.3 %

IEC PUBLICATION New IEC Publication 60599 Ed. 2.0 b: Mineral oil-impregnated electrical equipment in service - Guide to the interpretation of dissolved and free gases analysis, concerning the interpretation of dissolved gas-in-oil analysis, was issued in 1999 as a result of the revision of IEC TC 10 of the previous IEC Publication 599, issued in 1978. It describes how the concentrations of dissolved gases or free gases may be interpreted to diagnose the condition of oil-filled electrical equipment in service and suggests future action. It is applicable to electrical equipment filled with mineral insulating oil and insulated with cellulosic paper or pressboard-based solid insulation. Information about specific types of equipment such as transformers (power, instrument, industrial, railways, distribution), reactors, bushings, switchgear and oil-filled cables is given only as an indication in the application notes. Publication may be applied only with caution to other liquid-solid insulating systems. In any

5

case, the indications obtained should be viewed only as guidance and any resulting action should be undertaken only with proper engineering judgment. [3] The main body of IEC Publication 60599 contains an in-depth description of the five main types of faults usually found in electrical equipment in service. The familiar gas ratios have been retained for the diagnoses, but with new code limits, while additional gas ratios are suggested for specific fault cases. More precise definitions of normal and alarm gas concentrations in service are indicated compared to old one. In the Annexes, examples of typical (normal) gas concentration values observed in service are given for six different types of equipment. Extensive databases of faulty equipment inspected in service and of typical normal values related to various types and ages of equipment and types of faults have been used for the revision of Publication 60599. [3] Classification of faults in IEC Publication 60599 is according to the main types of faults that can be reliably identified by visual inspection of the equipment after the fault has occurred in service [3]:

o partial discharges (PD) of the cold plasma (corona) type with possible X-wax formation, and of the sparking type inducing small carbonized punctures in paper.

o discharges of low energy (D1), evidenced by larger punctures in paper, tracking, or carbon particles in oil.

o discharges of high energy (D2), with power follow-through, evidenced by extensive carbonization, metal fusion, and possible tripping of the equipment.

o a thermal fault below 300 ºC if paper has turned brownish (T1), above 300 ºC if paper has carbonized (T2).

o thermal faults above 700 ºC (T3), evidenced by oil carbonization, metal coloration, or fusion.

The number of characteristic faults is thus reduced from nine in the previous IEC Publication 599 to five in new IEC Publication 60599. In the Table 1 there are shown the old IEC Publication 599 gas ratio codes and the nine fault types that can be detected according to the codes. Identification of Faults in Service Using New IEC Publication 60599 The three basic gas ratios of IEC 599 (C2H2/C2H4, CH4/H2, and C2H4/C2H6) are also used in IEC 60599 for the identification of the characteristic faults. The ratio limits have been made more precise, using the data of Annex 1 in IEC 60599, in order to reduce the number of unidentified cases from around 30 % in IEC 599 to practically 0 %. Unidentified cases occurred in IEC 599 when ratio codes calculated from actual DGA results would not correspond to any of the codes associated with a characteristic fault. Graphical methods that allow one to follow more easily and more precisely these cases, as well as the evolution of faults with time, are also described. A more detailed version of the Triangle method, updated from a previously published version, is also included to the publication. [3]

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Table 1. Old IEC Publication 599 gas ratio codes and fault types according to the codes. [7]

In addition to the three basic interpretation ratios, two new gas ratios have been introduced in IEC 60599 for specific diagnoses: the C2H2/H2 ratio, to detect possible contamination from the on-load tap changers (OLTC) compartment (when > 3), and the O2/N2 ratio, to detect abnormal oil heating/oxidation (when <0.3). The limit below which theCO2/CO ratio indicates a possible involvement of paper in the fault has been made more precise (< 3). Finally, other sources of gas, not related to a fault in service (mainly, H2) are also indicated. [3] Triangle Method The Triangle graphical method of representation is used to visualize the different fault cases and facilitate their comparison. The coordinates and limits of the discharge and thermal fault zones of the Triangle are indicated in Figure 5. Zone DT in Figure 5 corresponds to mixtures of thermal and electrical faults. Readers interested in visualizing their own DGA results using

7

the Triangle representation should preferably use triangular coordinate drawing paper for better precision. The Triangle coordinates corresponding to DGA results in ppm can be calculated as follows: %C2H2 = 100x / (x+y+z); %C2H4 = 100y / (x+y+z); %CH4 = 100z / (x+y+z), with x = (C2H2); y = (C2H4); z = (CH4), in ppm. [4]

Figure 5. Coordinates and fault zones of the Triangle. [4] ON-LINE MONITORING Gas chromatographic diagnostics of power transformers has been recognised in the world for some time as the most efficient physical-chemical on-line diagnostic method for determination of potential thermal or electrical faults in transformers. Today the method is increasingly complemented by sensor on-line monitoring of transformers. This analysis is described as an on-line diagnostic testing method because the sampling of oil as well as the performance of the analysis can be carried out during normal operation of the power transformer, in other words, without temporary disconnection. [9] Monitoring of the state of transformers with various on-line sensors built directly into transformers or their insulation has been gradually put forward for the detection of disturbances and sudden faults. Operation of these sensors is based on the measurement of different signals. The development of on-line sensors for monitoring the state of transformers proceeds in two main directions. - Sensors for a certain typical gas (hydrogen, acetylene), which enable us to monitor the development trends of the gas in question. Certain sensors draw attention to specific disturbances. Hydrogen sensors thus draw attention to partial discharges and discharges with low energy, whereas e.g. acetylene sensors draw attention to discharges with high energy.

8

Neither of the two types of sensor mentioned draws attention to a thermal disturbance or a thermal damage of paper insulation. - More complex sensors, analytical instruments in their own right, detect all gases typical of certain faults in transformers. These draw attention to disturbances in transformers to the same extent as results of a DGA and should allow for on-line diagnostics. However, due to their complexity these instruments belong to an entirely different price class and their application makes sense only in special cases. Examples of such analysers include Transformer Nursing Unit (TNU) - a mobile unit for temporary on-line monitoring of typical gases in critical situations, and Transformer Monitoring & Management System (TMMS) developed for permanent installation in more important transformers. [9] Gas chromatographic diagnostics of power transformers based on DGA has been improved since it was introduced, and it still holds the most important place in the monitoring of transformers in on-line diagnostics. Experience has led to the conclusion that, in order to make a proper diagnosis in interpretation of results of a DGA, it is necessary first for each individual transformer to establish “normal” concentrations and, where these are exceeded, to use the method of ratios to determine the type of fault. In addition to these typical ratios, it is necessary to consider the speed of development of gases, the possibility of contamination and numerous data from the operation and maintenance of a transformer. On-line gas monitoring is being increasingly used to complement this. [9] METHODS OF INTERPRETATION Dissolved gas analysis (DGA) has long been the standard on-line tool used by engineers to determine the condition of power transformer. The popularity of DGA stemmed from the fact that testing is performed without disrupting the transformer’s operation. When DGA was conceived back in the 1960s, it was heralded as huge success with millions of pounds of losses being avoided by early detection of faults. However, its failures were almost as spectacular as it successes and it soon became apparent that DGA was by no means a complete solution. Many attempts have since been made to refine the decision process used to guide DGA engineers in their evaluations; such attempts include expert systems and analysis of the data using e.g. artificial neural networks. Typically, when a DGA engineer examines DGA data, they will compare the values that they have with the decision rules of several traditional analysis methods. What the engineer then does is to make subjective decisions and allowances based on what they see, i.e., does the data fit any of the decision criteria, if not how close is the data to those criteria? [5] Existing approaches for the interpretation of dissolved gas data relate gaseous composition to the condition of power transformers via the utilization of ratio-based schemes or artificial intelligence (AI) techniques. However, these approaches still contain some limitations. [6]

o Conventional Approaches Several renowned DGA interpretation schemes are, for example, Dörnenburg Ratios, Rogers Ratios, Duval Triangle, and the IEC Ratios. These schemes have been implemented, either in modified or improvised format, by various power utilities throughout the world. The implementation of these schemes requires the computation of several key gas-ratios. Fault diagnosis is accomplished by associating the value of these ratios with several predefined conditions of power transformers. Before subjecting the dissolved gas data for interpretation, a decision has to be made on whether fault diagnosis is necessary based on the comparison of

9

dissolved gas concentrations with a set of “benchmark” concentration values, which are also referred to as the typical values of gas concentration. If all dissolved gas concentrations are below these typical values, then the power transformer concerned can be regarded as operating in a faultless manner. These “benchmark” values should be calculated from a large historical DGA database, if available, based on the 90% or 95% thresholds. Although well received by power utilities, there are several limitations pertaining to the foregoing ratio-based approaches. [6] Table 2. Key gas ratios of conventional DGA interpretation schemes. [6]

o AI Approaches Attempts have been made to utilize artificial intelligence (AI) techniques to perform diagnosis of transformer condition based on the dissolved gas information. The intention of these approaches is to resolve some inherent limitations of the conventional interpretation schemes and to improve the accuracy of diagnosis. Single AI approaches only involve the utilization of one AI technique. The most common AI technique within this category is the supervised neural network (NN). Other AI approaches applied for DGA interpretation are of hybrid nature. Hybrid-AI approaches such as fuzzy expert system (FES) and the combined expert system (ES) and NN are more promising due to the fact that fuzzy logic (FL) or NN is used to tackle the ambiguity of conventional DGA interpretation schemes, which are integrated into the foregoing approaches, and expert experiences are incorporated to improve the credibility of diagnosis. [6] Conventional DGA theory The most important aspect of fault gas analysis is taking the data that has been generated and correctly diagnosing the fault that is generating the gases that have been detected. The three traditional DGA methods are (i) Roger’s Ratio Method (RRM), (ii) Dornenburg’s Ratio Method (DRM) and (iii) the Key Gas Method (KGM). All three of the methods have their theory routed in organic chemistry and base their diagnoses on matching the temperature generated by a fault to a general fault type. Put simply, each fault type typically generates a fault temperature within a prescribed range, the more severe the fault the higher the temperature. Because the insulating oil used in power transformers is organic (i.e., composed primarily of hydrocarbons), certain fingerprint gases are generated at specific temperature ranges, therefore allowing the traditional methods to identify a possible fault temperature range and therefore the possible fault type. The KGM actually uses four characteristic charts

10

that represent typical relative gas concentrations for four general fault types: overheating of cellulose (OHC), overheating of oil (OHO), partial discharge (PD) or arcing. The other two methods use ratios of the same fingerprint gases to try and pinpoint specific temperature ranges. The fingerprint gases used are again carbon monoxide (CO), hydrogen (H2), methane (CH4), ethane (C2H6), ethylene (C2H4) and acetylene (C2H2). One of the earliest methods is that of Dornenburg in which two ratios of gases are plotted on log-log axes (Fig. 6). The area in which the plotted point falls is indicative of the type of fault that has developed. [1]

Figure 6. Dornenburg’s Ratio Method [1] Grey model In the past, forecasting approaches have mostly used time series like least-squares regression or neural network models like back propagation based neural networks. Generally, these traditional forecasting models need a large amount of input data. However, the DGA of a power transformer is usually done only once in every year by power companies due to inspection cost consideration, so the historical database is very limited. The traditional forecasting methods are not appropriate for application in this field. The grey dynamic model (GM model) is particularly designed for handling situations in which only limited data are available for forecasting while system environment is not well-defined and fully understood. The GM model has been proved successful in many forecasting fields. For example, due to few historical dissolved gas records (only one test value for a year), a modified grey model (MGM) is proposed to predict the trend of dissolved gases. Then, future faults of power transformers can be directly identified by the fault diagnosis techniques, so that we can switch them out safely and improve the reliability of power systems. [7] The tested results show that the proposed method is simple and efficient. The grey theory describes random variables as a changeable interval number that varies with time factors and uses ‘colour’ to represent the degree of uncertainty in a dynamic system. If a system whose information is completely clear is called as a white system. In opposition, if a system whose information is not clear at all is called as a black system. In other words, if a system whose

11

information is partly clear or partly unclear is called as a grey system. The grey forecasting model is one of the applications of the grey theory. Instead of analyzing the characteristics of the grey systems directly, the grey model exploits the accumulated generating operation (AGO) technique to outline the system behaviours. The AGO practice may reduce the white noise embedded in the input data from statistics. [7] Due to the lack of sampling data, the grey model is very useful to set up the accurate forecasting model, since it can work with very little data. According to the field test results, it is shown that the proposed method can, not only provide the high accuracy model of the transformer oil-dissolved gas; it can also combine with other fault diagnosis method to detect useful information for future fault analysis. In addition, the calculation of the proposed method is fast and very simple and can be easily implemented by PC software. [7] Fuzzy model The criteria used in dissolved gas analysis are based on crisp value norms. Due to the dichotomous nature of crisp criteria, transformers with similar gas-in-oil conditions may lead to very different conclusions of diagnosis, especially when the gas concentrations are around the crisp norms. To deal with this problem, gas-in-oil data of failed transformers were collected and treated in order to obtain the membership functions of fault patterns using a fuzzy clustering method. All crisp norms are fuzzified to linguistic variables and diagnostic rules are transformed into fuzzy rules. A fuzzy system originally is used to combine the rules and the fuzzy conditions of transformers to obtain the final diagnostic results. It is shown that the diagnosing results from the combination of several simple fuzzy approaches are much better than traditional methods especially for transformers which have gas-in-oil conditions around the crisp norms. [2] ER (evidential reasoning) Algorithm A novel approach to the analysis and handling of dissolved gas analysis (DGA) data from several traditional methods, namely Roger’s Ratio Method, Dornenburg’s Ratio Method and the Key Gas Method, is presented also in Spurgeon paper. Ideas taken from fuzzy set theory are applied to ‘soften’ the fault decision boundaries employed by each of the three methods. This has the effect of replacing traditional Fault or No Fault crisp reasoning diagnoses, with a set of possible fault types (i.e., those fault types distinguishable by one particular method) and an associated probability of fault for each. These diagnoses are then considered as pieces of evidence ascertaining to the condition of the transformer and are aggregated using an evidential reasoning (ER) algorithm. The results are presented as probabilities of four possible general fault types: overheating of cellulose (cellulose degradation), thermal faults, partial discharge and arcing (corona). Finally the remaining belief is assigned to the possibility that no fault exists. The results show that the pseudo fuzzy representations of the traditional methods, perform adequately over a wide range of test values taken from actual failed transformers, and that the overall system can effectively combine the evidence to produce a more meaningful and accurate diagnosis. [5] The new approach generates subjective judgments such as these by using fuzzy membership functions to soften the decision boundaries that are currently utilized by the traditional methods. More precisely, crisp decision boundaries imply that the probability of fault can

12

either be zero or one. Softening such boundaries using appropriate functions means that the probability of fault can take on any value in the closed interval [0, 1]. By changing the boundaries in this way, the belief that an engineer has that a transformer is faulty can be represented by a single value. [5] In the case of DGA there is uncertainty in the accuracy of the diagnoses provided by the traditional methods. What ER does is provide a mathematical framework for combining such uncertain information (and subjective judgments). By considering each piece of information as evidence either supporting or denying a hypothesis, the validity of all possible hypotheses can be calculated. In the case of DGA, each hypothesis corresponds to a possible fault condition and the validity to the chance that this may be the condition of the transformer, e.g. 20 % chance the transformer has suffered from or is currently suffering an arcing fault. [5] Test clearly shows the power of the ER algorithm to combine effectively all of the available evidence from the three diagnosis methods and provide an array of possible faults, mimicking the natural reasoning process of a DGA engineer. It also demonstrates the practicality of using fuzzy membership functions for generating subjective beliefs in a simple manner, based on only two mathematical functions. The potential of this system lies in the fact that, whereas other systems treat the problem as one of classification, ER treats the problem as one of reasoning based on the DGA data. The flexibility of the tree structure used to make the decision and the algorithm for combining the evidence means that the system can be extended easily to encompass new diagnosis techniques by simply adding extra branches, parallel to the ones currently used. [5] CONCLUSIONS The technology presently exists and is being used to detect and determine fault gases below the part per million level. However there is still much room for improvement in the technique, especially in developing the methods of interpreting the results and correlating them with incipient faults. It is also important to realize that even though there is further need for improvement in the technique, the analysis of dissolved fault gases represents a practical and effective method for the detection of incipient faults and the determination of their severity. In addition to utility companies, many industries and installations that have on-site transformers are recognizing that the technique of dissolved fault gas analysis an extremely useful, if not essential, part of a well developed preventative maintenance program. [1] Obvious advantages that fault gas analyses can provide are summarized blow [1]:

1. Advance warning of developing faults 2. Determining the improper use of units 3. Status checks on new and repaired units 4. Convenient scheduling of repairs 5. Monitoring of units under overload

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REFERENCES [1] DiGiorgio, Joseph B., Dissolved Gas Analysis of Mineral Oil Insulating Fluids, NTT –

Technical Bulletin. 28.6.2005, available in www.nttworldwide.com/tech2102.htm [2] An-Pin Chen & Chang-Chun Lin, Fuzzy approaches for fault diagnosis of transformers,

Fuzzy Sets and Systems, 2001, Vol 118, No 1, pp 139-151. [3] Duval, M. & dePabla, A., Interpretation of gas-in-oil analysis using new IEC publication

60599 and IEC TC 10 databases, IEEE Electrical Insulation Magazine, 2001, Vol 17, No 2, pp. 31-41.

[4] Duval, M., A review of faults detectable by gas-in-oil analysis in transformers, IEEE Electrical Insulation Magazine, 2002, Vol 18, No 3, pp. 8-17.

[5] Spurgeon, K., Tang, W.H., Wu, Q.H., Richardson, Z.J. & Moss, G., Dissolved gas analysis using evidential reasoning, IEE Proceedings on Science, Measurement and Technology, 2005, Vol 152, No 3, pp. 110 – 117.

[6] Thang, K.F., Aggarwal, R.K., McGrail, A.J. & Esp, D.G., Analysis of power transformer dissolved gas data using the self-organizing map, IEEE Transactions on Power Delivery, 2003, Vol 18, No 4, pp. 1241-1248

[7] Wang, M. H. & Hung, C. P., Novel grey model for the prediction of trend of dissolved gases in oil-filled power apparatus, Electric Power Systems Research, 2003, Vol 67, No 1, pp. 53-58.

[8] Ward, S.A., Evaluating transformer condition using DGA oil analysis, Conference on Electrical Insulation and Dielectric Phenomena, 2003, Annual Report, pp. 463-468.

[9] Varl, A., On-line diagnostics of oil-filled transformers, Proceedings of 14th IEEE International Conference on Dielectric Liquids, ICDL 2002, 7-12 July 2002, pp. 253-257.

[10] http://www.powertech.bc.ca/cfm quoted 30.6.2005

Dielectric Diagnostics Measurements of Transformers

DIELECTRIC DIAGNOSTICS MEASUREMENTS OF TRANSFORMERS

Xiaolei Wang Institute of Intelligent Power Electronics

Helsinki University of Technology Otakaari 5 A, FIN-02150 Espoo, Finland

Phone: +358 9 451 4965, Fax: +358 9 451 2432 E-mail: xiaolei@cc.hut.fi

TABLE OF CONTENTS

1 Introduction ........................................................................................................................ 1

2 Transformer insulation structure ........................................................................................ 2

3 Dielectric diagnostics measurement method...................................................................... 3

3.1. Return Voltage Measurement (RVM)........................................................................ 4

3.2. Polarization and Depolarization Current method (PDC) ........................................... 6

3.3. Frequency Domain Spectroscopy (FDS).................................................................... 8

3.4. Comparison and conclusion on three dielectric measurement approaches ................ 9

3.5. Influences on dielectric measurement ...................................................................... 10

4 Conclusions ...................................................................................................................... 10

5 References ........................................................................................................................ 10

1 INTRODUCTION

During recent years, the diagnostics of power system equipments, for example, transform-ers, has gained great research attention. Diagnostics refers to the interpretation of data and off-line measurements on transformers. It is normally used either for determining the actual condition of a transformer, or as a response to a received warning signal [1]. Generally, the current transformer diagnostics approaches become more and more advanced, which can be classified by different fault types, e.g., thermal, dielectric, mechanical, and degradation re-lated faults. To detect ageing phenomena of transformers, there are several methods that can be employed, such as partial discharge measurement and gas-in oil analysis, etc. Dielectric Diagnostics Method (DDM) is one distinguished approach to asseting ageing condition of transformers, which represents a family of methods used for characterization of dielectric materials as well as practical insulation systems [2].

The focus of this report is on the basic principle of DDM as well as its three important measurement methods. Firstly, the general insulation structure of transformers will be de-scribed in the following section. Three dominated DDM-based measurement approaches,

1

Dielectric Diagnostics Measurements of Transformers

Return Voltage Measurement (RVM), Polarization and Depolarization Current (PDC), and Frequency Domain Spectroscopy (FDS), will be introduced in section 3. Finally, there will be a short conclusion and discussion.

2 TRANSFORMER INSULATION STRUCTURE

The life span of the magnetic device highly depends on its insulation system, an amalgam of different insulating materials, processes, and interactions. Generally, the insulation system of power transformers consists mostly of mineral oil and cellulose paper. With the age in-creasing, the oil/paper insulation of transformers will degrade due to thermal, oxidative and hydrolytic factor. One aging indicator is the water content in the solid part of the insulation. Increased water content accelerates the deterioration of cellulose through depolymerisation, and causes bubble formation resulting in electrical breakdown as well [3].

To estimate the humidity of transformer insulation, one needs a data library of dielectric

properties ( ∞ε , dcσ , and f(t)) of oils and pressboard at different humidity content. This data

library is necessary to calculate the dielectric response of the composite duct insulation, and for comparing with the measurement results. In frequency domain, the material of trans-former is characterized by a complex frequency and temperature depended permittivity, as shown in (1).

),(),(),( TiTT ωεωεωε ′′⋅−′= (1)

In the frequency range of interest, for transformer oil the real part is constant ( =r 2.2ε ), and

the imaginary part is dominated by the contribution from the DC conductivity. However, in

the time domain the material is characterized by the power frequency permittivity ( rε ), DC

conductivity ( DCσ ), and dielectric response function f(t) [4].

the

idit lectric permittiv-

ity,

In addition, the dielectric response is influenced by the insulation structure, as shown in Fig.1 [5]. In the winding configuration (Fig.1 (a)), the section of insulation duct consists of cylindrical shells of pressboard barriers separated by axial spacers. In the modeling of the combination of oil and cellulose in the duct (Fig.1 (b)), parameters X and Y are defined as the relative amount of solid insulation (barriers) and spacers respectively. Generally,barriers fill 20-50% of the main duct, and the spacers fill 15-25% of the circumference.

Based on the abovementioned material and geometric properties of composite system, the dielectric response can be derived. In the time domain (PDC and RVM methods), the calcu-lation is based on the known response function ƒ(t), and it also depends on temperature and hum y. In the frequency domain (FDS method), the composite die

ductε , of the insulation duct is calculated as the following function [5].

2

Dielectric Diagnostics Measurements of Transformers

barrieroilbarrierspacer

duct XXY

XXYT

εεεε

ωε+

−−

++

−= 1

11),( (2)

Fig. 1. (a) Section of an insulation duct of a power transformer with barriers and spacers, (b) schematic representation of the barrier content and the spacer coverage in the insula-

tion duct.

On the right side of (2), the permittivity of the oil, spacers, and barriers deduced from the measurements on the insulation model, are complex quantities dependent on frequency, temperature, and humidity. The materials properties are varied until a good fit with the measured values is achieved [6].

3 DIELECTRIC DIAGNOSTICS MEASUREMENT METHOD

It is well known that one important application of Dielectric Diagnostics Method (DDM) is to asset the humidity in the insulation system of transformer. Due to its oil/paper insulation structure, transformer shows the characteristics of polarization and conductivity. DDM can work in such a field dominated by interfacial polarization at the boarders between cellulose and oil, and cellulose and oil conductivity. Fig. 2 illustrates a basic diagram for electric measurements [7]. In figure 2, the Low Voltage (LV) and High Voltage (HV) winding ter-minals of transformer are connected together as a two-terminal test object to the DDM in-strument.

DC voltage, DC current, AC voltage, and AC current can be measured to evaluate dielectric response phenomena, which develop three widely employed dielectric diagnostics meas-urement methods:

• Recovery (Return) Voltage Method (RVM),

• Dielectric spectroscopy in time domain, i.e., Polarization and Depolarization Currents method (PDC),

• Frequency Domain Spectroscopy analysis (FDS), i.e., measurements of electric ca-pacitance and loss factor in dependency frequency.

3

Dielectric Diagnostics Measurements of Transformers

Fig. 2. Basic circuit diagram for dielectric measurements.

DC voltage measurements are applied as recovery voltage measurements after charging the insulation with a DC voltage. The derived diagnostic method is called the RVM. A series of recovery voltage measurements with increased charging time leads to the so called “Polari-zation Spectrum” which is commonly used to evaluate the moisture content of cellulose. A DC current measurement will record the charging and discharging currents of insulation. They are known as the PDC. AC voltage and current measurements are derived from the well-known Tangent Delta measurements. However the frequency range is much enhanced especially to low frequencies, e.g. 0.1MHz. The derived measurement method is the FDS [8].

In this section, we will discuss these three methods in detail.

3.1. Return Voltage Measurement (RVM)

The dielectric measurements can be performed in both frequency and time domain. The fea-ture of time domain measurement methods is applying a step voltage across the sample. Figure 3 shows a simplified diagram of dielectric response measurement in time domain for a power transformer. For the RVM, s3 closed and s1, s2 open [9]. In this case, the RVM is coupled to the low voltage terminal and the high voltage windings and the tank are grounded. The principle of the measurement is that the test object is first charged for a given time, then discharged for half the charging time and after that the return voltage is measured under open circuit conditions.

Assume a DC step voltage is applied to the initially completely discharged system:

4

Dielectric Diagnostics Measurements of Transformers

⎪⎩

⎪⎨⎧

≤≤≤>

=t t 0

tt0 t0 0

)(1

10UtU (3)

DC

S1

S2S3

A

V

HV

LV

Fig. 3. Simplified Diagram of Dielectric Response Measurement.

When the step voltage is applied during period 10 tt ≤≤ , the charging (polarization) current

is generated

⎥⎦

⎤⎢⎣

⎡+= )()(

000 tfUCti r

pol εσ (4)

where is the permittivity of vacuum of the dielectric material, and mF /10854.8 120

−×=ε

rσ is the average conductivity of the composite insulation system. The response function of

the composite insulation f(t) describes the fundamental memory property of the dielectric system and can provide significant information about the insulation material. Afterwards the step voltage is disconnected from the insulation (grounded), and the discharging (depolari-zation) current is generated as shown in (5) [10].

[ ])()()( 100 ttftfUCtidepol +−= (5)

The source of the recovery voltage is the relaxation of remaining polarization in the insula-tion system, giving rise to an induced charge on the electrodes. The polarization spectrum is established by performing a series of recovery voltage measurements with stepwise charg-ing and discharging time. For each sequence in the spectrum, the peak of recovery voltage

as well as the initial rate of rise of the recovery voltage rV dtdVr are recorded and plotted

versus the charging time used [5].

Figure 4 demonstrates such a polarization spectrum. In this RVM, a transformer is charged initially for 0.5 second, after that in every next cycle the charging time is doubled, until 1024 seconds. The ratio of charging to discharging time is a constant two. Charging and discharging current and return voltage data are recorded for every test cycle.

5

Dielectric Diagnostics Measurements of Transformers

Fig. 4. Dielectric response measurement of return voltage.

A return voltage spectrum, indicating the insulation condition, can be drawn with the return voltage and the central time constant from each test cycle. Figure 5 illustrates such return voltage spectra, and the operation times of these transformers are list in Table 1. The peak in the spectrum can provide indication of the insulation condition. The spectrum also pro-vides the range of the response in the time domain [9]

Fig. 5. Typical return voltage spectra.

Table 1. Transformer operation time in Fig.5.

Transformer T4 T5 T6

Age (year) 3 38 33

3.2. Polarization and Depolarization Current method (PDC)

The polarization current is also obtained by applying a step voltage between the HV and LV windings during a certain time. The charging current of the transformer capacitance, i.e. in-

6

Dielectric Diagnostics Measurements of Transformers

sulation material, called polarization current is generated. The depolarization current is measured with power supply removed. The connection of the PDC is same as that of the RVM in the time domain measurement illustrated in Fig. 3. For this case, polarization cur-rent measurement is performed with s1 closed, s2 and s3 open. For the depolarization cur-rent measurement, s2 is closed, and s1 as well as s3 are open. Figure 6 demonstrates the cur-rent waveform during the instant of voltage application which decreases during the polariza-tion to a certain value given by the conductivity of the insulation system [9].

Fig. 6. Polarization and depolarization current waveform.

From the (4) and (5), we can conclude that both polarization and depolarization current con-sider dielectric response function. The DC conductivity of the test object can be estimated from the measurements of polarization and depolarization current. However, it is easier to employ the depolarization current due to no DC current involved. Figure 7 shows the meas-ured polarization currents from some moisture-conditioned samples. Obviously, the ampli-tude of long term DC polarization current is quite sensitive to the moisture content in paper insulation, which demonstrates that polarization current measurement is capable of assess-ing the insulation moisture lever [9].

Fig. 7. Measured polarization current from moisture conditioned samples.

7

Dielectric Diagnostics Measurements of Transformers

3.3. Frequency Domain Spectroscopy (FDS)

In the frequency domain measurement, a sinusoidal voltage is applied, and the complex di-electric constant is determined from the amplitude as well as phase of the measured current flowing through the test object. The dielectric susceptibility can be considered as the re-sponse function in time domain, which is related through the following Fourier transform function [11].

∫∞ −=′′−′=

0)()()()( dtetfXiXX tj

sssωωωω (6)

The susceptibility is a complex function of frequency, and is related to the relative permit-tivity as shown in (7)-(9).

ωεσωωεωεωεωε0

)()()()()( iXiXi ssrrr −′′−′+=′′−′= ∞ (7)

)()( ωεωε sr X ′+=′ ∞ (8)

ωεσωωε0

)()( iX sr +′′=′′ (9)

In (7), the imaginary part of the complex relative permittivity (loss part) contains both the resistive (DC conduction) losses and the dielectric (polarization) losses, and that at a given frequency it is impossible to distinguish between the two. However, the resistive part is dominant at low frequency. In this case, the imaginary part of the complex relative permit-tivity has a slope of and the real part is constant. Based on this, the conductivity of the test object could be calculated. Another way of presenting the measured information of a FDS is to use the loss factor [5].

1−ω

Alternatively, loss factor method is usually employed to present the measured information. Loss factor is the frequency dependent ratio of imaginary and the real parts of the complex permittivity as shown in (10) [11]. The geometrical independency of the rest object makes the loss factor important to study when the object geometry is unknown.

)()(tan

ωεωεδ

r

r

′′′

= (10)

Figure 8 demonstrates the loss factor of the four units as a function of frequency. Obvi-ously, T11 (1973) is similar to T12 (1973), and T41 (1977) is similar to T42 (1977). How-ever, the main reason of the difference is that the oil conductivity of T11/T12 is higher than that of T41/T42. With the fixed oil conductivity, the influence of the moisture content of the cellulose is illustrated in Fig. 9. The moisture content is around one percent in T11/T12, and lower in T41/T42 [6].

8

Dielectric Diagnostics Measurements of Transformers

Fig. 8. Loss factor as a function of frequency for different transformers.

Fig. 9. Loss factor for different moisture contents of the cellulose.

3.4. Comparison and conclusion on three dielectric measurement approaches

Nowadays, all of these three methods are widely employed for the diagnostics of power transformer insulation, with commercial available or test set-up instruments sometime.

The RVM method is a useful but more sensitive to systematic errors than the others. It is a high impedance input method, and leakage currents on the bushings could easily corrupt the measurements. Basically, the PDC is a non-destructive method, and is same as the insula-tion resistance measurement. If the currents are low the method can be sensitive to offset currents and interference in the field. The oil conductivity can be deduced from the initial current and board conductivity affected by moisture is from the final current. The FDS method has the advantage of including the loss factor and capacitance measurement. There-fore, it is easy to detect fault in the test set-up. The inherent small bandwidth makes the method relatively insensitive to interference and there is no need for a high voltage power supply [6].

9

Dielectric Diagnostics Measurements of Transformers

3.5. Influences on dielectric measurement

Although dielectric diagnostics techniques have been improved recent years, there are still some factors affect measurement reliability and stability of analysis results. These influ-ences are also the main source of error. For instance, some essential issues of them are list as following [7].

• Insulation temperature

• Migration processes

• Decreasing oil conductivity

• Parallel current paths

• Temperature compensation in analysis software

• Interpretation of measurement data

• Comparison to moisture equilibrium charts

• Measuring time

4 CONCLUSIONS

Due to their remarkable characteristics, the RVM, PDC, and FDS are the three dominant dielectric diagnostics approaches to asses the power transformer insulation. In this report, their basic principles and analysis results are discussed respectively. The analysis demon-strates that these diagnostics methods are useful for off-line assessment of power trans-former insulation. However, these approaches are still influenced by some conditions and source errors. Recently, more and more research work are concerning about the condition factor to the measurement.

5 REFERENCES

[1] C. Bengtsson, “Status and trends in transformer monitoring,” IEEE Transactions on Power Delivery, vol. 11, no. 3, pp. 1379-1384, 1996.

[2] U. Gafvert, “Dielectric response analysis of real insulation systems,” in Proceedings of the IEEE International Conference on Solid Dielectrics, Toulouse, France, July 2004, pp. 1028-1037.

[3] Y. M. Du, B. C. Zahn, A. V. Lesieutre, and S. R. Lindgren, “A review of moisture equilibrium in transformer paper-oil systems,” IEEE Electrical Insulation Magazine, vol. 15, no. 1, pp. 11-20, 1999.

[4] A. K. Jonscher, Dielectric Relaxation in Solids. London, UK: Chelsea Dielectrics Press, 1984

[5] CIGRE, “Dielectric response methods for diagnostics of power transformers,” IEEE Electrical Insulation Magazine, vol.19, no.3, pp.12-18, 2003.

10

Dielectric Diagnostics Measurements of Transformers

[6] U. Gafvert, L. Adeen, M. Tapper, P. Ghasemi, and B. Jonsson, “Dielectric spectros-copy in time and frequency domain applied to diagnostics of power transformers,” in Proceedings of the International Conference on Properties and Applications of Dielec-tric Materials, Xi’an, China, June 2000, pp. 825-830.

[7] K. Maik, and F. Kurt, “Reliability and influences on dielectric diagnostic methods to evaluate the ageing state of oil-paper insulations,” in Proceedings of International Con-ference on Advances in Processing, Testing, and Application of Dielectric Materials, Wrocław, Poland, Septermber 2004.

[8] W. S. Zaengl, “Dielectric spectroscopy in time and frequency domain for HV power equipment, part I: theoretical considerations,” IEEE Electrical Insulation Magazine, vol. 19, no. 5, pp. 9-22, 2003.

[9] T. Y. Zheng, and T. K. Saha, “Analysis and modeling of dielectric responses of power transformer insulation,” in Proceedings of IEEE Power Engineering Society Summer Meeting, Chicago, IL, July 2002, pp. 417-421.

[10] T. K. Saha, and P. Purkait, “Effects of temperature on time-domain dielectric diagnos-tics of transformer,” Australian Journal of Electrical & Electronics Engineering, vol. 1, no. 3, pp. 157-162, 2004.

[11] T. K. Saha, “Review of modern diagnostic techniques for assessing insulation condi-tion in aged transformers,” IEEE Transactions on Dielectrics and Electrical Insulation, vol. 10, no. 5, pp. 903-917, 2003.

11

1

ASSET MANAGEMENT IN POWER SYSTEMS

CONDITION MONITORING OF GENERATOR SYSTEMS

Matti Heikkilä

Vaasa

matti.heikkila@pvo.fi

2

CONTENTS

1. INTRODUCTION........................................................................................................ 3

2. ON LINE CONTINUOUS MEASUREMENTS..................................................... 4

2.1 Vibration Measurements ............................................................................................. 4 2.2 Temperature Measurements ....................................................................................... 4 2.3 Protection Relays......................................................................................................... 5 2.4 Measurement of Shaft Movement.............................................................................. 5

3. CONDITION MONITORING................................................................................... 5

3.1 On-line Partial Discharge (PD) Measurements ...................................................... 6 3.2 Measuring of Isolation Resistance ......................................................................... 11 3.3 Mechanical Inspection .............................................................................................. 11 3.4 Tan Delta Monitoring System ................................................................................... 11

4. REFERENCES........................................................................................................... 13

3

1. INTRODUCTION The generator is one of prime devices in a power plant. The arrangement of the condition monitoring of generator is extremely important to avoid many failures, because the fixing of the generator takes very long time and the costs of the lost production are significant. So with the condition monitoring systems it is possible to follow the generator condition and to plan the revision in right time. Normally there are many measuring devices in generator, which follow the condition of generator during production. Such measurements are e.g.:

• Vibration measurements • Temperature measurements of generator windings • Protection relays • Measurement of generator shaft movement

Additionally for previous measurements, it is normal that during the revision the resistance of generator isolation is measured and mechanical inspections are made. The modern condition monitoring system is an on-line partial discharge (PD) monitoring system. The aim of partial discharge measurement on windings of rotating electrical machines is the assessment of the insulating conditions of the windings. The measurement and subsequent analysis of PD generates important information, which pay attention to the type of defects that may occur due to local over-stressing within an insulating system. These measurements are therefore well suited for non-destructive diagnosis of various dielectric materials e.g. the insulation of generator windings. With regard to risk assessment and early planning of preventive maintenance for rotating electrical machines it is necessary to get reliable information on the type and intensity of the partial discharges and therefore on the actual conditions of the winding insulation. Without performing meaningful diagnostic measurements a critical PD activity may, over a short or medium time range, lead to an unexpected machine failure, which causes considerable expenses be due to unscheduled downtimes and the replacement and/or reconditioning of damaged components. In the following Figure 1 is shown an example of air-cooled generator, in which have made all those measurement, which are mentioned in this seminar report.

4

Figure 1. Air cooled generator, 250 MW, 277 MVA, 15 kV

2. ON LINE CONTINUOUS MEASUREMENTS 2.1 Vibration Measurements If the generator vibration is very high it may cause damage to the shaft bearings of generator and also to the mechanical construction of generator. So it is normal that there are installed vibration measurement sensors (axial and radial x/y) into generator bearings. Into automatic controlling system is also set the limits to permitted vibration level; the automation system gives an alarm, if the limit is exceeded and the shut down command, if the vibration is too high. 2.2 Temperature Measurements The temperature of generator windings is followed by temperature measurement sensors (normally Pt100 sensors), which are connected into automatic controlling system. Into automatic controlling system is set limits for alarm and shut down commands.

5

2.3 Protection Relays Generator and its electrical systems are protected by protection relays. Modern generator has equipped with following protection relays:

• Generator over current • Generator over voltage • Generator under voltage • Generator differential protection • Generator overload • Generator reverse power • Generator pole slipping • Generator under excitation • Generator minimum impedance • Generator negative phase sequence • Generator under frequency • Generator over frequency • Generator stator earth fault 90 % and 100 % • Generator circuit breaker failure • Transformer bus earth fault • Unit transformer over current • Unit transformer differential protection • Unit transformer oil temperature • Unit transformer winding temperature • Unit transformer tab switch failure • Unit transformer gas relay • Unit transformer over pressure • Block transformer differential protection • Block transformer gas relay • Block transformer oil temperature • Block transformer winding temperature • Overall differential protection

2.4 Measurement of Shaft Movement Generator and turbine has equipped with shaft movement measurement system. High temperature, vibration and other reasons can cause that the shaft moves a little bit or has heat expansion. In that case the bearings function is not good. Measurement of shaft movement gives an alarm or shut down command if needed.

3. CONDITION MONITORING Generator condition must be followed and measured during running and during revision. On Chapter 2 are explained normal on-line measuring systems. Modern condition monitoring system, Partial Discharge (PD) monitoring system is explained in following Chapter 3.1. In

6

Chapter 3.2 is explained isolation resistance measurement, in Chapter 3.3 mechanical inspections and in Chapter 3.4 Tan delta measurements. 3.1 On-line Partial Discharge (PD) Measurements Partial discharges are small sparks which occur in high voltage insulation systems. Partial discharges (PD) in electrical machines arise

• in cavities in insulation systems when subjected to high electric fields, • in overcharged gas-filled gaps in the vicinity of windings, • in the area of faulty anti-corona systems.

Considered individually, the partial discharges in electrical machines are harmless. However, when high energy partial discharges occur permanently they can destroy any insulation system. The result is dielectric breakdown leading to machine failure. Partial discharge (PD) monitoring is the continuous acquisition and analysis of electrical discharges on an object interacted by voltage. The aim of partial discharge monitoring on rotating electrical machines in operation, is the early recognition of discharge phenomena which are dangerous for operation. The partial discharge monitoring system very sensitively detects and analyses location and cause of partial discharges arising in the machine while in operation. Partial discharge phenomena can not be recognized with any of the conventional protection systems. Partial discharge monitoring measurements are particularly recommended

• for key machines as generators • for planning condition-based maintenance • where enhanced operational reliability is called for • for fully automatic plant operation • in cases of severe operating conditions

In the partial discharge system are needed couplers which are connected to the machine to be monitored, the PD signal processor and the data acquisition and analyzing system as is shown in the following Figure 2.

7

Figure 2. Partial Discharge Monitoring System. [16] The partial discharge signals are decoupled by a special high-frequency current transformer or a capacitive coupler on a low-voltage side (star point) of the stator winding and depending on the machine, also by capacitive couplers on the high-voltage side. The measured signals are transmitted through special screened cables to the PD signal processor. The data acquisition and analyzing system is permanently connected to the PD signal processor. The system has also equipped to record the voltages of the objects being monitored. The individual components of PD- monitoring system are [16]:

• Couplers: capacitive couplers and high-frequency current transformers • PD signal processor: multiplexer for many high-frequency channels and voltage

channels including amplifier and filter • Data acquisition and analyzing system: components for digitizing and processing

the measured data, and for controlling all system components and functions. In this unit, disturbances are automatically eliminated; characteristic values and trends are calculated and indicated.

• Screened high-frequency signal cable with TNC / BNC connectors • Matching unit: only needed if couplers are only periodically connected to

measurement system • PD calibrator: needed during installation only

8

How does the PD measuring system works [16]: The signals originating in the insulation system are decoupled by the couplers. The signals are then transmitted from the couplers to the PD signal processor. There the analogue signals are prepared for fast scanning. The filters and amplifiers are set automatically by the computer of the data acquisition and analyzing system. Characteristic values, in conformity with IEC Publication 60270, are acquired by the data acquisition and analyzing system. After the processing, the signals are digitized at a scanning rate of up to 40 million measurements per second (40 MHz) (tuned to the filter characteristic of the PD signal processor) in the data acquisition and analyzing unit. After a complex noise recognition there follows an automatic suppression of time and frequency-stable and generator-specific interference. Analytic processes then statically evaluate the numerous partial discharges and reduce the immense amount of data. Besides the usual worldwide known evaluation criteria, additional quantities describing the distributions are also determined and stored. In the processing result are used tree dimensions as we can see in Figure 4:

• Phase angle � (deg) • Number of pulses n • Apparent charge q (nC)

Long-time trends of these characteristic values permit continuous comparison with reference values to be made. Should the measured values exceed reference values an alarm is immediately initiated. The result of the analysis supply information on the kind, origin, intensity, and frequency of occurrence of the discharges. This information is then evaluated to determine the influence on the operational reliability of the machine. The benefits of PD measuring system:

• Partial Discharge Monitoring provides high operational reliability for the machine.

• PD Monitoring system supplies important information on the actual state of the machine in operation an on any critical changes arising. This information can not be detected by any other protective or monitoring system.

• When the results are positive, the time until the next maintenance inspection can be extended. When the results are critical the cause of the fault can be recognized and eliminated at an early stage before an actual failure occurs in the machine.

• Failure costs can be avoided and maintenance costs minimized so that the investment for PD Monitoring system pays for itself in a short time.

Examples of PD measuring: In the following Figure 3 is shown the measuring arrangements of air cooled generator 250 MW, 277 MVA, 15 kV:

• Measuring with generator half load without inductive load • Measuring with generator half load with 80 MVAr inductive load • Measuring with generator full load without inductive load • Measuring with generator full load with 80 MVAr inductive load

9

VL2: Planned PAMOS measurement time schedule 21.4.2002

0

50

100

150

200

250

0:00 2:00 4:00 6:00 8:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00

Time

[MW

]

Test Output

Normal Output

Inductive Output

Test Output

Normal Output

Inductive Output

Figure 3. Measuring arrangements for PD measurement. In the following Figure 4 are measuring results: The normally visible inner PD’s are superimposed by base noise of the machine. This is a sign of very low PD activity, especially of the inner PD’s. Inner discharges are typical for all mica based and resin impregnated insulation systems. The sources are small gaps and voids within the ground wall insulation. Since the mica based insulation is highly PD resistant such inner PD’s are regarded as rather harmless. They are classified as uncritical because of the low level within the same range as the base noise band. PD sources coming from the insulation system of the machine cannot be detected. Therefore the measured generator seems to be in good condition at time of the measurement. Needle shaped disturbances from the excitation device superimpose all PD readings. These disturbances are narrow and appear on a low level, so that they don’t mask any real PD phenomena from the machine. Those disturbances are marked in the following measuring result Figure 4.

10

Figure 4. PD Measuring results of air cooled generator with full load 235 MW and minimum reactive load 4 MVAr.

11

3.2 Measuring of Isolation Resistance At least once per year for example during the revision or during long shut down, it is good to measure the isolation resistance of generator windings. In the following Figure 5 is shown one example of insulation resistance measuring.

VL3 eristysvastus

-2000

0

2000

4000

6000

8000

10000

12000

0 10 20 30 40 50 60

Aika/min

Eris

tysv

astu

s/M

oh

m.

Figure 5. Generator windings insulation resistance measurement. The insulation resistance measuring has made with Metriso 5000 measuring device with 2500 V voltage and measuring time was one hour. Measuring time must be long enough for the insulation resistance to reach the final value. 3.3 Mechanical Inspection During generator revision it is necessary to make mechanical inspections for generator. The making of the following inspections are depending on the running hours of generator:

• Mechanical inspection of breakings and cracs of different mechanical constructions • Retaining rings must check by ultrasonic devices after about 70 000 running hour if

retaining rings are made of old material. In new generators has used new material (18% Mn / 18% Cr) and it is not necessary to check retaining rings any more, because the new material don’t split or break any more.

• Generator stator windings slots and end-windings must check after about 50 000 running hours. Stator windings might get loose and they must be tightened again.

3.4 Tan Delta Monitoring System Tan delta measurements are the most suitable for oil-paper insulated high voltage (HV) cables used in transformers, generators, bushings and other electrical devices. So in the new generator, in which has used mica- insulation and other new insulation materials, it is better to use partial discharge measurements.

12

The tan delta measurement is a comparative method in which the reference measurement device is normally constructed from high voltage capacitors. The measured values are also compared to previous measurements of examined cables or devices.

Figure 6. Basic arrangement for the Tan delta measurement of C. [14.] HV = Operating Voltage C = Capacitance of Insulation (tan delta) (corresponding to dielectric losses of insulation) Ca = Additional Capacitance for Signal Conditioning Ur = Reference Voltage Um = Measurement Signal Voltage MS = Monitoring System

MS

13

4. REFERENCES

1. Miomir U. Kotlica, On-Line Monitoring of Power Generator Systems, KES

International Ltd, Toronto Canada, 1998. 2. Yunsok Lim, Jayoon Koo, Joenseon Lee, Wonjong Kang, Chaotic Analysis of Partian

Discharge (CAPD) – A novel approach to identify the nature of PD source, SMDT Lab., Dept. of Electrical Engineering, Hanyang University, Korea, 2001.

3. Y. Han and Y. H. Song, Condition Monitoring Techniques for Electrical Equipment –

A Literature Survey, IEEE Transactions on Power Delivery, vol. 18, No 1, January 2003.

4. P. H. F. Morshuis, R. Bodega, M. Lazzaroni, F. J. Wester, Partial Discharge Detection

Using Oscillating Voltage at Different Frequencies, Delft University of Technology, The Nederlands, 2002.

5. X. Ma, C. Zhou and I. J. Kemp, Wavelet for the Analysis and Compression of Partial

Discharge Data, School of Engineering, Science and Design, Glasgow Caledonian University, UK, 2001.

6. Ümmühan Basaran, Mehmet Kurran, The Strategy for the Maintenance Scheduling of

the Generating Unit, Anadoly University Eskisehir, Turkey, 2003.

7. A. M. Leite da Silva, G. J. Anders, L. A. F. Manso, Generator Maintenance Scheduling to Maximize Reliability and Revenue, IEEE Porto Power Tech Conference, 2001.

8. S. Cherukupalli, R. A. Huber, C. Tan, G. L. Halldorson, Application of Some Novel

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