Analysis of On-line Power Cable Signals Donald M Hepburn1*, Chengke Zhou1, Xiaodi Song1, Guobin Zhang1 and Matthieu Michel2

1 Glasgow Caledonian University, Glasgow, G4 0BA, UK 2 EdF Energy, West Sussex, RH10 1QQ, UK

*E-mail : dmhe@gcal.ac.uk

Abstract--For many years identification of incipient Partial Discharge (PD) faults in power cables has been made through off-line investigation techniques. The periodic monitoring of power cables resulted in unexpected failures, with consequent financial penalties for the utilities. More recently, in an effort to allow continuous asset management of the medium voltage cable network to be carried out, on-line monitoring systems are being installed with the aim of reducing unexpected failures.

This paper presents work on the analysis and handling of data acquired from an on-line system. A short review of on-line vs. off-line cable PD monitoring will be presented, in terms of their respective advantages and disadvantages. The authors’ experience of applying Wavelet-based denoising techniques to extract PD data from external noise interference will be presented. Analysis of PD activity and noise interference, with respect to time-of-day, will offer insight into the challenges relating to these systems. Finally, a means of handling the vast amounts of data and of acquiring knowledge from on-line condition monitoring data will be discussed.

Index Terms--Cables, Monitoring, Partial discharges, Power distribution, Wavelet

I. INTRODUCTION

VER many years the structure of distribution cables, and the materials used as insulation within the cable, have

changed. Paper insulated lead covered (pilc) cables with circular cross section developed to contain shaped conductors and the insulation varied from the oil-paper combination to that of polymeric materials [1]. In early diagnostic regimes the determination of the presence of incipient partial discharge (PD) faults was determined using DC voltage tests. In polymeric cables it was found that the DC test could be destructive, due to high voltage levels required to induce PD and to the build-up of space charge within the cable insulation during the test and poor correlation between results of DC testing and operational stress [2], [3]. When the cable is reinstated into the working regime, the remnant space charge strongly affects the electrical stress within the materials and, in some cases, leads to failure. More modern test systems apply a very low frequency (VLF) voltage to the cable being investigated [2]. The voltage applied for VLF testing is akin to that in standard AC commissioning tests, well below those for DC tests, and that the variation in voltage during VLF tests reduces the space charge build-up. These forms of test require that the cable under test is disconnected from the distribution network and, hence, are off-line monitoring procedures. Given the more competitive marketplace which exists for utilities, the higher levels of power usage and expectation of continuity of service in the consumer base, removal of distribution cable

from service for investigation has become more of a challenge. Failure of utilities to effectively monitor and maintain the underground distribution network can, however, have dramatic effects, e.g. 174,000 residents in New York lost power in July of 2006 [4]. The outage resulted in severe reprimand for the board of directors, for failing to address pre-existing faults and for poor maintenance, and large financial penalties to the company.

More modern systems for monitoring cable networks use on-line data capture, e.g. [5], [6] and, as a result, the cable is stressed at working voltage rather than the overstress applied during VLF and DC tests. For three-phase cables this results in the electrical field distribution conforming to that of operational systems rather than a single phase being stressed whilst the others are tied to ground. In addition, as the cable is in operation, the thermal and physical stresses which pertain to normal operation are also present. One component which affects the collection and interpretation of PD signal data is the presence of electrical noise; this will be addressed within the paper. A second variation between on-line and off-line data collection is that during on-line measurement the cable’s load current will affect the temperature of the insulation: correlation between load and PD activity will be considered in this work.

II. DATA COLLECTION SYSTEM

The data collection system employed is an IPEC ASM monitor using high frequency current transformers (HFCT) [7]. This provides high speed data collection over a distributed PD sensor network, with the HFCT PD sensors positioned on the earth strap of the MV cable. The systems are placed in a number of substations within the EdF MV network. Access has been provided to allow data from sites to be analysed using the algorithms developed at Glasgow Caledonian University. Each data file from the IPEC system contains the signal information covering a 20 milli-second (ms) period, equivalent to 1 cycle of a standard UK AC sinusoidal wave.

Data provided by EdF also included the Load Current being carried by each circuit during the periods during which PD signals were being collected. Figs. 1 and 2 show the variation in Load Current for two sites over a period of 1 week. Although there are differences in magnitude for the site loads, the form of load variation is consistent for the sites: in both cases load is lowest between 04:00 and 06:00 and highest between 18:00 and 23:00. Weekend load rises later and does not to reach the morning plateau until 10:00 rather than 08:00 mid-week. The data is found to be self-consistent for the days

O

2008 International Conference on Condition Monitoring and Diagnosis, Beijing, China, April 21-24, 2008

978-1-4244-1622-6/08/$25.00 ©2007 IEEE

of the week, this is not included in the paper. The data from site 1 has been selected as an exemplar for analysis.

50

60

70

80

90

100

110

120

130

140

0 5 10 15 20

Time

Load

(A

mps

)

Mon

Tues

Wed

Thurs

Fri

Sat

Sun

Fig. 1. Load current data from site 1, twice per hour over one week.

45

55

65

75

85

95

105

115

125

0 5 10 15 20

Time

Load

(A

mps

)

Mon

Tues

Wed

Thurs

Fri

Sat

Sun

Fig. 2. Load current data from site 2, twice per hour over one week.

III. SIGNAL PROCESSING

To allow the signals collected from the various sites to be analysed it was necessary to reduce the influence of the electrical noise. This was carried out through implementation of a Second Generation Wavelet Transform (SGWT) algorithm. An explanation of the algorithm’s implementation can be obtained from [8]. The implementation of the SGWT, however, leads to distortion of the original signal waveform when strong noise is present and significant data is lost during this operation. To overcome the signal distortion which had been introduced, a second process was implemented to reconstruct the pulse to its original form. An exemplar of the data collected over one 20ms period is shown in Fig. 3: as can be seen there is a strong presence of noise within the signal.

Fig. 4 shows an expanded segment of the data, isolating a feature which is believed to be a PD pulse. As can be seen there is a complex, multi-component, sinusoidal electrical noise present in the signal. The electrical noise is eliminated through application of the algorithms.

The result of the application of both the SGWT denoising and the reconstitution algorithms being applied to this data is shown in Fig. 5.

From a comparison of Figs. 4 and 5, it can be seen that the denoised and reconstructed signals provide a much clearer indication of the PD magnitude and the characteristic form of

the current pulse. The characteristics of the pulse shapes are being used to categorise and group the pulses. The work is at an early stage and is not reported here.

Fig. 3. Original signal data, exemplar from site.

Fig. 4. Expanded segment of original signal data in Fig. 3.

Fig. 5. Denoised and reconstructed version of signal from segment in Fig. 4.

IV. RESULTS

Exemplars of 20ms signal segments, from the monitored site which is being analysed here, are shown in Figs. 6, 7 & 8. Fig. 6 was collected at 05:05 on a Saturday morning, Figs. 7 and 8 were collected at 00:32 and 01:04 on the following day.

By inspection of the data and comparative analysis of the signal over all time periods for which data is collected, it has been ascertained that there is little variation in the background noise energy. The characteristic frequencies of the noise are, however, found to be site dependent. Work is undertaken to

the

he PD signals are bei g generated from more than one phase.

Fig. 6. 20ms of signal from site at 05:05, Saturday.

Fig. 7. 20ms of signal from site at 00:32, Sunday.

Fig. 8. 20ms of signal from site at 01:04, Sunday.

try to ascertain the sources of the components of the noise in cable circuits. Knowledge of the sources of noise will assist with improvements to signal processing.

By applying the SGWT and reconstruction algorithms to the 20ms data files, similar improvement can be made to the clarity of the phasal distribution of the PD pulses as was evident in the reconstruction of the individual PD shape. The processed data, in Figs. 9, 10 and 11, are provided to show the improvement of the data from Figs. 6, 7 and 8 respectively.

Standard analysis considers the clustering of PD activity in relation to a single phase sinusoid [9], [10]. With a 3-phase system, however, the PD has to be considered in relation to the voltage level for each of the three phases.

As was generally expected of the data, e.g. PD activity in Figs. 9, 10 and 11, there is an indication that the PD activity in

cable changes with time. Fig. 9 shows PD distributed across the 20ms period of the

AC sinusoid, through comparison of a three phase voltage diagram and the relative position of the PD signals, no correspondence was found between the PD and a single AC phase cycle. Analysis, thus, indicates that t

n

Fig. 9. Denoised and reconstructed data from signal in Fig. 6.

Fig. 10. Denoised and reconstructed data from signal in Fig. 7.

Fig. 11. Denoised and reconstructed data from signal in Fig. 8.

Similar consideration of Figs. 10 and 11, PD activity in the same cable at other times, indicates change in PD frequency and magnitude. Fig. 10, when compared with Fig. 9, shows reduced PD frequency at 3ms & 13ms and increased intensity of clustering centered at 5ms & 15ms. In Fig. 11 the clusters at 5ms & 15ms are more clearly defined with the reduction of PD across other times. The presence of these clusters and

ggestive of PD activity pertaining to one phase of the

riods investigated wh

to relate the load cu

stimate which of the three phases is generating the signals.

previously used to good effect by the authors [11], [12].

ient for analyzing HFCT sig

ctivity will allow correlation of signals and faults to be mad

reduced intensity at other times within the AC period is strongly su

cable. The indication from this analysis is that PD activity from

one phase has increased over the time peilst that from other phases has reduced. It should be pointed out that, although the total current in

the cable is known at different time periods, as shown in Figs. 1 and 2, no data is available for the individual phases of each cable. The possibility of an imbalance in the load to the three phases of the cable makes it impossible Fig. 12. PD intensity relative to one phase in 3-phase system.

rrent to the PD in the individual phases. The second challenge for analysis of the PD is to determine

on which phase of the cable the increased activity is occurring. Through inspection of Figs. 12 and 13, it can be seen that the clusters of activity could be assigned to the peaks of one phase or, alternatively, to the rising edge of a second phase. Further information on the structure of the fault is required before it would be possible to e

V. DATA STORAGE AND HANDLING

The authors are currently developing software to allow the characteristics of the PD pulse shapes and relevant timestamp data to be stored, rather than the raw data. From this database of PD information and fault analysis information, being gathered from the cables used in this work, data reduction and knowledge acquisition algorithms are to be developed. The algorithms are to be based on the Rough Set th

Fig. 13. PD intensity relative to second phase in 3-phase system.

VII. REFERENCES

[1] D. McAllister, Ed., Electric Cables Handbook, Granada Publishing, UK, 1982. ISBN 0-246-11467-3

[2] J. M. Pang and Seah, "The Danger of DC High Voltage Test for XLPE Cables at Site". Available at http://www.jmpangseah.com/chapter-1.pdf

eory, [3] M.Kreutzer, H.Schlapp and P.M.Waeben, “On Site MV-Cable Testing with AC-Voltages”, 12th Int. CIRED Conf. on Electrical Distribution, 17-21 May 1993, p. 3.12/1-3.12/4.

[4] Dept. of Public Service Staff Report, “Investigation of July 2006 Equipment Failures and Power Outages in Con Edison’s Long Island City Network in Queens County, New York”, Case 06-E-0894, Feb. 2007. Available http://www3.dps.state.ny.us/pscweb/WebFileRoom.nsf/Web/F813FD973CA2310285257267004B9E83/$File/LIC_FINAL_REPORT_FEB_9_07.pdf?OpenElement

VI. CONCLUSIONS

On-line detection of PD activity is possible and practical to implement, however, the interpretation of the signals collected is still in its infancy. The high level of noise in the on-line signals makes interpretation of PD activity a challenge for the majority of installations. Signal denoising and reconstruction allows observations to be made on the level of activity occurring and estimation of the phase which is giving rise to the activity. The algorithms developed by the authors are shown to be effective and effic

nals from in-situ measurement. Further experimental work and theoretical analysis is

required to provide information on the inter-relationship between possible situations of defects giving rise to the PD and the electrical field changes across the 3-phase system. Ideally single-phase excitation of 3-phase cables in an installation which has known characteristic changes to PD

[5] F. Steenis, P. van der Wielen and B. Kaptein, “Permanent On-line Montiroing of MV Power Cables Based on Partial Discharge Detection and Localisation – an update”, 7th Int. Conf. on Power Insulated Cables, Jicable, Paris, 24-28 June 2007, A4.1.

[6] C. Walton and R. Mackinlay, “PD Monitoring – A Critical Tool for Condition-Based Assessments”, Transmission and Distribution World Magazine, Dec. 2003, p.38-46

[7] IPEC data sheet, “How the ASM works”, Available at http://www.ipec.co.uk/asm/resources/assets/factsheets/How_the_ASM_works-The_System.pdf

[8] X. Song, C. Zhou, D.M. Hepburn, G. Zhang and M. Matthieu, “Second Generation Wavelet transform for Data Denoising in PD Measurement”, IEEE Trans. Diel. & El. Insul., Vol.14, No.6, Dec. 2007, p.1531-1537.

[9] D.A. Nattrass, “Partial Discharge Measurement and Interpretation”, IEEE Electrical Insulation Magazine, Vol.4, No.3, 1988, p.10-23

[10] F.H. Kreuger, E. Gulski and A. Krivda, “Classification of Partial Discharges”, IEEE Trans. El. Insulation, Vol.28, No.6, 1993, p.917-931 a e.

[11] C. Zhou, M. Michel, D.M. Hepburn and G. Zhang: “Rough Set Theory for Data Mining in an On-line Cable Condition Monitoring System”. CIRED 2007, Vienna, Austria, May 2007

[12] G. Zhang, C. Zhou, D.M. Hepburn and X. Song: “Rough Set Theory for Knowledge Acquisition in Turbine Generator Lock Joint Fault Diagnostics”, 15th ISH, Slovenia, Aug. 2007