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DEVELOPMENT OF THE REMOTE MONITORING AND DIAGNOSIS SYSTEM FOR HIGH VOLTAGE SUBSTATION
J. B. KIM*, M. S. KIM, J. R. JUNG, W. P. SONG I. D. KIM
HYOSUNG CORPORATION Doowon Tech. College
(Republic of Korea) (Republic of Korea)
In this paper, we roughly describe the principles and structures for the sensor applications and diagnostic algorithm with together networking and monitoring system for GIS & MTR in 154kV substation. Firstly, we describe the detailed specifications of monitoring items for GIS & MTR and applied sensors. For the diagnosis of CB(Circuit Breaker) mechanical operation, we used auxiliary CT(current transformer) and auxiliary contacts as a sensor. It was performed experiments in general and extreme conditions to establish the standard values that can be evaluated the condition of mechanical operation, and then was obtained the standard values to prevent the faults. Techniques that monitors leakage current of a zinc-oxide lightning arrester is described with results of field test. Methods are also described to detect partial discharge and ground fault location, as well as to monitor gas pressure. For the purpose of monitoring the internal condition of transformer, we select the solved gas, insulation oil temperature, and OLTC motor driving current as monitoring items. Keywords: Gas-Insulated-Switchgear, Remote-Monitoring-Diagnosis, Partial-Discharge-Monitoring, UHF-Sensor, Solved-Gas, 1. Introduction As the recent trend of the power system based on the IPP(Independent Power Producer) / the distributed generation and the advancement of IT(Information Telecommunication) technology, the needs of domestic/abroad on the GIS(Gas Insulated Substation) attached the diagnosis function have been rapidly increased. “A monitoring and diagnostic system for power apparatus”being promoted introduction by KEPCO (Korea Electric Power Corporation) and the other demander has aims such as the following ; detecting the malfunction with a highly efficient sensors and a signal processing techniques, in case of abnormal progress it prevents a emergent shut-off of electricity by warning, sets up a plan of maintenance / repair by the diagnosis standard, prevention for recurrence of an accident and a speedy recovery of an accident by judging both the cause of fault and a fault location. Adding to that, the development of low-price and common intelligent diagnostic system for preventing accident of the existing established power apparatus have been on the needs for pre-estimate of the lifetime and for economic maintenance repair plan. The intelligent diagnostic system is one which diagnose the degradation characteristic of power apparatus with on-line using a highly efficient sensors and the signal processing techniques,
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Session 2004B5-108
simultaneously optical communication and S/W techniques[1]. For the development this system, techniques such as the following are necessary. � Sensor application techniques to detect the malfunction of power apparatus � Diagnostic algorithm design techniques for judging the malfunction of power apparatus � Networking and display technique to show the conditions of power apparatus � Techniques by which reducing the price of the system within 10% of power apparatus price
In order to commercialize this system, as showed in Fig. 1, � the sensor application techniques of a power apparatus, � techniques of diagnostic algorithm, � techniques of networking and display system are necessary. In now, we have completed the development in connection with items �, and for the case of items � and �, it has been a state which has stepped onto orbit in some degree by researching for the last several years. In this paper, we roughly describe the intelligent diagnostic algorithm together with networking and display system in the diagnostic system for GIS & MTR in 154kV substation.
GIS Part
1. CB mechanical operation
2. Gas leakage
3. LA degradation
4. Partial Discharge (UHF)
Transformer Part
1. Solved Gas
2. Oil temperature
3. Current of OLTC Motor
Power Apparatus Sensor Application DAS & Network System Monitoring system
Intranet
YongYong--YeunYeunFactoryFactory
ChangwonChangwonFactoryFactory
1. Solved Gas
Analysis System
2. Temperature
Sensor
3. CT
1. CT+Aux, S/W
2. GDT
3. LA leakage CT
+amp
4. UHF Sensor Analysis Panel
DAU2
DAU1Local DAS
1. MCB
2. AIM
3. DIM
4. SIM
5. CCU
with PC e-hope
Intranet
Outside1. MCB:Main CPU Board
2. AIM:Analogue Input Module
3. DIM:Digital Input Module
4. SIM:Serial Input Module
5. CCU:Communication
Control Unit
154kV S/S in Anyang Factory
154kV S/S in Ulsan FactoryIntranet
* e-hope Intranet system is applied at 2nd stage.
* System in Uisan Factory will be accomplished
at 3rd stage
PDM Panel
PC + S/W
PC
with display
S/W
GIS Part
1. CB mechanical operation
2. Gas leakage
3. LA degradation
4. Partial Discharge (UHF)
Transformer Part
1. Solved Gas
2. Oil temperature
3. Current of OLTC Motor
Power Apparatus Sensor Application DAS & Network System Monitoring system
Intranet
YongYong--YeunYeunFactoryFactory
ChangwonChangwonFactoryFactory
1. Solved Gas
Analysis System
2. Temperature
Sensor
3. CT
1. CT+Aux, S/W
2. GDT
3. LA leakage CT
+amp
4. UHF Sensor Analysis Panel
Analysis Panel
DAU2
DAU1Local DAS
1. MCB
2. AIM
3. DIM
4. SIM
5. CCU
with PC e-hope
Intranet
OutsideOutside1. MCB:Main CPU Board
2. AIM:Analogue Input Module
3. DIM:Digital Input Module
4. SIM:Serial Input Module
5. CCU:Communication
Control Unit
154kV S/S in Anyang Factory
154kV S/S in Ulsan FactoryIntranet
* e-hope Intranet system is applied at 2nd stage.
* System in Uisan Factory will be accomplished
at 3rd stage
PDM Panel
PC + S/W
PC
with display
S/W
Fig 1. The layout of remote monitoring &diagnostic system
2. Intelligent Diagnostic System For 154kV Substation At present, the supervision items of monitoring / diagnostic system for 154kV substation� as shown in Table 1, are 7 items, and we are in promoting the development in accordance with consumer's demand by the method to which add a supervision item. It is being developed the basic 4 items among the above 6 items using the method such as the followings. 3. Monitoring Items of GIS 3.1 Mechanical Operation of CB Operating method of CB for 170kV GIS is hydraulic, and the specification of it is prescribed by velocity and operation time. Optical slit method, which was used by means of diagnostic method that have the items of velocity and operation time, have been proposed by other maker[2]. However, we chose a simple method of using auxiliary CT and auxiliary contacts, because the charge due to expensive equipment addition and an unconfirmed long-term reliance[3].
Monitoring part of power apparatus Item of diagnosis Applied Sensor
CB operating mechanism
CB mechanical operation condition CT+ Aux. contacts
SF6 gas Gas density measurement Gas density transmitter Lightning
arrest Leakage current
measurement CT(leakage current) GIS
Partial discharge
Electronic-wave measurement UHF sensor
Internal side of tank Solved gas Analytical apparatus
Insulation oil Oil temperature Temperature indicator MTR
OLTC OLTC motor driving current CT/A
A device for diagnosing CB operating characteristics transmits signal to remote associated with connection/disconnection of CB auxiliary switch applied specific voltage for maintaining CB parts and predicting CB lifetime and measures current amplitude and current flowing time of trip/closing coil for avoiding malfunction of CB. It is looked out a malfunction of the mechanical operation by observing the current and operation time using the auxiliary CT and auxiliary contacts, which was added in a control circuit of GIS. It is possible to detect the operation characteristics using the current amplitude / duration time and the operation time of “a” & “b” contacts during closing and trip operation. Firstly, it should be established the standard values to diagnose the 170kV GIS. From this reason, we examined the initial measurement of the above contents against an 800kV GIS for Dangjin T/P. It was carried out the measurement of the operation time ∆T of a and b contact for switching operation of the CB installed in Dangjin T/P. The range of fluctuation is the results measured for 10 times and ∆T is within the limits of 1ms. From these results, we were able to set limits of each content in the early stage (no aging state). Then, after considering the variations of hydraulic pressure, coil-voltage, contacts and other factors, we could determine the 3rd diagnosis standard values through the process in Fig. 2.
3.2 SF6 gas tightness of GIS Since SF6 gas is the electric insulation medium of GIS, gas-filling condition have to be checked every time. SF6 gas density transmitter (GDT) compensated temperature was used to monitor the gas tightness in every gas section. It can be also estimated the location of the internal arc occurrence.
Fig 2. The determination process in standard value
TABLE 1. THE ITEMS OF INTELLIGENT DIAGNOSIS SYSTEM FOR 154KV S/S
When SF6 gas pressure is explosively increased in a particular gas section, internal arc might be occurred in that section. A SF6 gas leakage leads to insulation accident. Therefore we can determine normality or abnormality about insulation by monitoring gas pressure continuously, wherein gas pressure affects current breaking and insulation performance of CB. The GDT(Gas Density Transmitter) placed on gas valve inside Local Control Panel transmits signal to remote associated with gas density. Thermal induced pressure changes are dynamically compensated and do not affect the output signal. The GDT generates a density proportional, standardized signal of 4~20mA. The GDTs are placed 5~10 a bay by gas spacings and power supply of the GDT is placed DAU by Local Control Panel.
3.3 The degradation characteristics of lightning arrest The lightning arrester for 170kV GIS is tank type insulated SF6 gas. We have to check whether it operates in stable conditions or not. The deterioration rate of internal ZnO component in lightning arrester is being monitored / diagnosed using the measurement of leakage current. We are examining the measurement method and type of leakage current ; total leakage current, resistive leakage current, harmonic leakage current[5]. Total leakage current is directly measured using simple method, but it is difficult to check the degradation level of arrester because the total leakage current is mainly composed of capacitive current. Resistive leakage current is able to be measured using differential amplifier, but it is necessary to compensate the effect of temperature. Harmonic leakage current is also directly measured using simple method like the total leakage current, but it is necessary to analyze the harmonic elements in the frequency domain. Via several tests, we could make out standard value, total leakage current 6mA, of the diagnosis algorithm for LA of 170kV GIS. To determinate the standard value of other leakage current, we are carrying out several experiments. It can be seen that the harmonic leakage current rapidly increased as compared with 60 Hz component depending on applied voltage. However, at the condition of the degradation, both leakage currents similarly increased depending on applied voltage. After that total leakage current is compared with resistive and harmonic leakage current for LA of 170kV GIS, we will decide the type of leakage current in order to optimally diagnose the degradation of LA. Fig. 4 shows a system configuration for measuring total leakage current, third harmonics of leakage current, and resistive current component and a photograph of LCM equipped with GIS.
0
3
6
9
12
15
18
21
-1 1 3 5 7 9 11 13
Pressure(kg/am2.G at 20, WIKA GD)
Out
put C
urre
nt(m
A)
TheoryNo1No2No3No4No5
Fig 3. The output characteristics of GDT & actually installed GDT inside LCP
Fig 4. System configuration of signal processing unit & actually installed LCM in GIS
3.4 Partial Discharge inside enclosure of GIS There has been reported a lot of results using an U.H.F sensor, an acceleration sensor, a ultrasonic sensor and an earthing current sensor against the partial discharge of GIS, but it is in difficulty to analyze the signal obtained from a sensor due to the noise problem on the substation. In Hyosung Co., from the results examined with various methods, we finally selected the U.H.F sensor so as to get the partial discharge signal that has a high frequency range distinguished from the noise of the substation. The signal of UHF sensor is converted into the quantity of the partial discharge using the technology of noise removal by digital filter. And we placed iPDM(Intelligent Partial Discharge Monitoring) device that can measure and diagnose partial discharge inside enclosure of GIS distinguished from substation noise. 1) Principle of Measurement � Measurement of Basic Noise
The device measures noise ranging from 500MHz to 1500MHz in the state of not equipped with sensor on spacer in order to eliminate noise such as external corona from the surroundings.
� Parameter Setting of Diagnosis Software - Filtering Range
Partial discharge generates signal of all frequency band. Diagnosis software eliminates a frequency band of measured basic noise. We set 830MHz~920MHz(Cellular phone), 1050MHz~1120MHz(Airplane), and 1240MHz~1380MHz(Radar communication signal) as frequency band of noise.
- Frequency of Diagnosis We set parameter so that the device diagnoses PD for 2min by acquiring data 6 times every 20 seconds(in case of abnormality, increase the repeat of diagnosis).
- Accuracy of Diagnosis In case measured value is above –55dBm(above 5pC), the device performs accurate diagnosis about occurrence of PD.
� System configuration
O p t i c a l
C a b l e
O p t i c a l
C a b l e
C o a x i a l
C a b le
C o a x ia l
C a b l e
U H F S e n s o r A
U H F S e n s o r B
D B 1
D B 2
D e t e c t i o n B o x P D M P a n e l
Fig 5. System configuration of iPDM
� Example of Measurement Result
Fig 6. PD occurring cause- diagnosis
Fig 7. PD value converted by pC
4. Monitoring Items of MTR 4.1 Solved Gas When the corona happens due to the internal fault of transformer, ie line short or overheating, the solved gas will be aroused in the insulation oil. Thus, it will be possible to diagnosis the internal condition of transformer by measuring ppm of the solved gas among the insulation oil. 1) The kinds of solved gas generated by fault We are showing the kinds of gas generated by fault in table 2. When the fault happens, we indicated ‘�’ mark for expression of gas generated by the corresponding fault. But it is difficult to confirm the internal condition of transformer in according to solved gas. Therefore, for diagnosing the internal condition of transformer, we are finally deciding the internal condition of transformer by carrying out various techniques by measuring the pattern of the solved gas and the inside inspection of transformer. 2) The solved gas analytical apparatus actually installed in 154kV MTR The output signal of solved gas analytical apparatus actually installed in 154kV substation is DC 4~20mA current. This signal corresponds to total sum of H, CO, C2H2, C2H4. And, recently we provide the real time monitoring system measuring the amount of H2, CO, CO2, CH4, C2H6, C2H4, C2H2 according to consumer’s demand.
The output signal is transferred to analogue input layer of DAU(Diagnosis Analytic Unit), and through DAS(Data Acquisition System) & network system, finally transferred to the remote monitoring room.
Table 2. The kinds of solved gas generated by fault
The kinds of fault The kinds of gas
Overheating of insulation oil
Overheating of insulation material
Arc resolution in oil
Arc resolution in material
H2 O O � �
CH4 � � O O
C2H6 O O - -
C2H4 � � O O
C2H2 - - � �
C3H6 � O O O
C3H4 O O - -
CO - � - �
CO2 - � - � 4.2 Temperature of insulation oil When the internal fault of transformer happens, for monitoring the increasing temperature of oil, we apply oil temperature indicator attached in transformer. we use the method of measuring resistance proportional to increasing of oil temperature as the principle of measuring oil temperature. This resistance value(0~200 ohm) is converted to DC 4~20mA through RTD, and this converted current value is transferred to analogue input layer of DAU through isolator.
Fig 8. The system configuration for measuring oil temperature
4.3 OLTC Motor driving current For the purpose of monitoring normal/abnormal condition of driving shaft connecting OLTC motor and OLTC tap changer, we measure the OLTC motor driving current by using CT/A(Current Transformer Assey). And measured data is transferred to analogue input layer of DAU.
Fig 9. The system configuration for measuring OLTC motor driving current
5. Monitoring System Monitoring sensors described above are connected to data acquisition system (DAS), which located in the local control room. DAS gathers data from each DAU. Host computer in the main control room
DAU
DAU
collects these data from DAS through network system, constructs database of monitored value, and recalculates them. Monitoring system in the main control room has WEB based graphic user interface (GUI), so not only in the substation control room but also in any place outside of substation where offers internet connections, the status of the substation can be monitored. 6. Expert System In order to organize an expert system that made preventative diagnostic systems for substation effectively, characteristic data of power apparatus and variety of data of fault relative phenomenon are needed. For this purpose, individual opinions of experts are collected, and knowledge base is set up. An inference rule is made so that expert system can predict possible fault at proper time, and proposes measures to rapid maintenances. Artificial neural networks have been applied to give expert system heuristic ability. Running substation produces massive data during its lifetime. Artificial neural networks continuously learn the relationship between these data and fault event and provide operator with more reliable decision-making. 7. Conclusion We reported the results of research about remote monitoring & diagnostic system for GIS & MTR in 154kV substation. First of all, after we roughly refer to the concept of intelligent diagnostic system for power apparatus, we described the specification and the situation of development. Specifically, we have reported the results of experiment against diagnostic algorithm for mechanical operation, gas tightness, degradation of LA and partial discharge. From these results, we could obtain the diagnostic algorithm of high reliability for 170kV GIS. And, for the purpose of monitoring the internal condition of transformer, we select the solved gas, insulation oil temperature, and OLTC motor driving current as monitoring items. Now, we are operating this system and storing the measuring data. We will improve our diagnostic skills for power apparatus by using these back-up data. References [1] T. Sakakibara, H. maehara, S. Maruyama, A. kobayashi and S. Kuninori, “Reliability verification of system applying a microprocessor in a high-voltage gas insulated substation”, IEEE Trans. on Power Delivery, Vol. 13, No. 4, 1105-1113, 1998 [2] K. Goto, T. sakakibara, I. Kamata and S. Ikeda, “On-line monitoring and diagnostics of gas circuit breakers”, IEEE Trans. on Power Delivery, Vol. 4, No. 1, 375-381, 1989 [3] Endre Mikes, “State of the art high voltage switchgear ; Availability, reliability, functionality and condition based maintenance aspects of circuit breakers with spring mechanism”� , CLDEM, 1996 [4]Catalogue of WIKA Gas Density Measurement, PM 02.31~PE 85.04, 2000 [5] S. Shirakawa, F. Endo, H. Kitajama, S. Kobayashi, K. Kurita, k. Goto and M. Sakai, “Maintenanace of surge arrester by a portable arrester leakage current detector”, IEEE Trans. on Power Delivery, Vol. 3, No. 3, 998-1003, 1998
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