On-line fault diagnosis of power substation using connectionist expert system

IEEE Transactions on Power Systems, Vol. 10, No. 1, February 1995

ON-LINE FAULT DIAGNOSIS OF POWER SUBSTATION USING CONNECTIONIST EXPERT SYSTEM

323

Hong-Tzer Yang Wen-Yeau Chang Ching-Lien Huang

Member Member

Department of Electrical Engineering National Cheng Kung University

Tainan, Taiwan R.O.C. 701

Abstract

This paper proposes a new connectionist (or neural network) expert system for on-line fault diagnosis of a power substation. The Connectionist Expert Diagnosis System has similar profile of an expert system, but can be constructed much more easily from elemental samples. These samples associate the faults with their protective relays and breakers as well as the bus voltages and feeder currents. Through an elaborately designed structure, alarm signals are processed by different connectionist models. The output of the connectionist models is then integrated to provide the final conclusion with a confidence level. The proposed approach has been practically verified by testing on a typical Taiwan Power (Taipower) secondary substation. The test results show that rapid and exactly correct diagnosis is obtained even for the fault conditions involving multiple faults or failure operation of protective relay and circuit breaker. Moreover, the system can he transplanted into various substations with little additional implementation effort.

Keywords: Connectionists, Expert System, Fault Diagnosis, Substation

1. Introduct ion

On-line fault diagnosis is needed for dynamic emergency COII- trol in the distribution automation system. In the process of emergency control, the fault situation should be first identified in order to take proper actions for system restoration as soon as possible. The fault situation to be identified includes the information about where and what the fault is, i.e., the fault section and the fault type. Based on the alarm signals of the protective system, the operators of a substation are required to estimate the fault situation immediately after the occurrence of fault. However, in the cases of multiple faults or failure operation of protective devices, the situation will lead to a large number of alarm signals. To tell at once the real situation and original cause of the fault is difficult for the operator, if not impossible [l].

Since heuristic rules of the system operators’ experiences are heavily relied on in the fault diagnosis, applications of rule-based expert system receive widespread studies [2-51. Although the rule-based approach offers powerful solution to the fault diagnosis, it suffers from some imperfections. Knowledge acquisition and knowledge base revision turn out to be quite tedious, often

94 WM 256-8 PWRS by the IEEE Power System Engineering Committee of the IEEE Power Engineering Society for presentation at the IEEE/PES 1994 Winter Meeting, New York, New York, January 30 - February 3, 1994. Manuscript submitted June 25, 1993; made available f o r printing January 4, 1994.

A paper recommended and approved

making the development of the system a lengthy process. Fur- thermore, it is quite hard to get the diagnosis system to improve its performance by automatic learning from experiences newly obtained [6].

One potential solution to these problems is the use of artificial neural networks (or connectionists model), as demonstrated in some recent papers [7,8]. A simple structure of neural networks was trained in [7] to interpret multiple alarms in a control center of substation. The problem of fault diagnosis was formulated as one of pattern recognition by identifying different combinations of relay and breaker states. In [SI, a neural networks approach to on-line fault section estimation in a control center of power system was proposed by the authors. Basic capabilities of neural networks in fault diagnosis were exhibited.

In this paper, a novel structure of connectionist expert system is proposed to on-line identify the fault situation of a power substation. The profile of the diagnosis system is similar to that of an expert system, hut having connectionist models for its knowledge base and thus different design philosophy of the inference engine.

To enhance the capability of the diagnosis system, an Auxil- iary Diagnosis Connectionists Model (ADCM) is included to help estimate the fault when definite or high confidence-level conclusion cannot he provided by the system only using the alarm signals from relays and breakers. The input data of the ADCM are the peak values of bus voltages and feeder currents. In other words, the alarm signals from the relay and breaker open/close states as well as the bus voltages and feeder currents are both employed as the symptom data for diagnosis. Also, in the special case of line-open fault that the protective relays equipped in the substation would not operate, the ADCM is used as the main diagnosis tool.

The proposed diagnosis system can automatically learn new fault scenarios by adding sample data into its training set, and can make reasonable generalization of learned scenarios. To demon- strate the effectiveness of the proposed approach, the system has been tested on a typical secondary substation of the Taipower System.

This paper is organized as follows. After the introduction, the problem of the fault diagnosis is addressed in Sec. 2. Next, the structure of the expert diagnosis system and the design of the individual units are described in Sec. 3. Results of testing our approach on a practical substation and some exemplar cases are discussed in Sec. 4. Finally our conclusions are given in Sec. 5 .

Abbreviat ions Used

ADCM Auxiliary Diagnosis Connectionists Model EMTP ElectroMagnetic Transients Program FDCs Forward Diagnosis Connectiouists BTCs BacliTracliing Connectionists C!Cs Comment Connectionists ADC Auxiliary Diagnosis Connectionist XOR Exclusive OR SCADA Supervisory Control And Data Acquisition

088S-89SO/9S/$o4.00 0 1994 IEEE

324

CO Over Current LCO Low Energy Over Current DR Differential Relay RC Recloser lJV Undervoltage

2. Fault Diagnosis in a Substat ion

When fault occurs, the alarm signals received in an automated substation would be the open/close status of the protective relays and breakers (binary signals) as well as bus voltages and feeder currents (analog signals). According to these binary and analog symptom patterns, the operators are required to estimate the fault problem. Basically, the task of the fault diagnosis is a process of pattern recognition.

In general conditions, a fault situation would result in a particular binary symptom pattern for the open/close states of relays and breakers. By correctly recognizing the symptom pattern, the possible fault situations can be straightforward identified. How- ever, for error-existing symptom patterns, or for patterns with failure operation of relay or breaker, the problem would be sophis- ticated. At this conjuncture, a fault situation may bring about several possible symptom patterns, and a symptom pattern con- fronted by the operators may be caused by a number of possible fault situations. In other words, the problem of the fault diagnosis would be one to one, many to one (several symptom patterns to one fault situation) or one to many (one symptom pattern to several fault situations) mapping problem, if the information of relays and circuit breakers is provided only 191.

In such cases, the problems need be solved with the aid of examining the peak values of bus voltages and feeder currents when a fault occurs. In this paper, both the binary and analog symptom patterns of the fault are used in the proposed diagnosis system. The identification result based on the binary patterns of relay and breaker open/close status is regarded as the primary conclusion, and that based on the analog signals as the auxiliary answer when definite or high confidence-level conclusion cannot be achieved by only using the information of relays and breakers.

Prior to dealing with such a fault diagnosis problem, the following assumptions are given:

1. Five categories of faults in a substation are considered, i.e., line-open, single line-to-ground, line-to-line, double line-to- ground and three-phase faults.

2. All relays and breakers have arrived at their final states, e.g.. the recloser has accomplished its reclosing duty cycle and any operations going to occur have taken place. The open/close states of the relays and breakers can therefore be represented as binary symptom patterns to identify the fault.

3. Operation of relay or breaker can fail, and the failure relay or breaker should be identified to inform the operator.

4. No relay operates by itself without a fault in its protection zone and no breaker opens by itself without associated relay's tripping signal.

3. Connectionist Expe r t Diaanosis Sys t em

3-1 Sys tem Outl ine

The structure of the proposed Connectionist Expert Diag- nosis System is shown in Fig. 1, which comprises User Inter- face, Connectionist-type Knowledge Base, Inference Engine, Data Preparation Unit and Auxiliary Diagnosis Connectionists Model. As noted, the profile of the proposed system is similar to that of

Fl SCADA

Fig. 1: Structure of the Connectionist Expert Diagnosis System

a general expert system, whereas the intrinsic structure is quite different. In the sequel, the individual units of the system will be described.

3-2 Creat ion of Training Samples

To begin with the construction of the system, the training samples for the connectionist models must first be created. The proposed diagnosis system is composed of two types of connectionists which possess binary and analog input forms, respectively.

For the binary input-form connectionist, the training sample data are created through the following steps:

1. Prepare the one-line diagram of the substation. List the possible faults that would occur in the substation.

2. Inquire the protective relaying scheme. For each fault, indicate its corresponding protective relays and breakers, which include primary and back-up protective devices, to form the elemental samples.

3. Associate the faults with the relay and breaker open/close states to create the training samples.

Table 1 shows a form of the training samples compiled by the User Interface which will be detailed later. It is noted in this table, the information of power system devices, configuration, and the relaying scheme is implicitly composed in the training samples. No effort is required to express the information in terms of explicit rules as done in the conventional expert system.

For the analog input-form connectionist (ADCM), the cre- ation of sample data follows the steps:

1. Set up the models of the substation and the distribution network.

325

Table 1: Form of Training Samples Compiled by User Interface

2. Simulate all possible faults by using ElectroMagnetic Tran- sients Program (EMTP) [lo] and obtain the peak values of bus voltage and feeder current corresponding to each fault.

3. Normalize the bus voltages and feeder currents such that their values are reduced to between 0.0 and 1.0.

3-3 User Interface

The User Interface serves a.s a communication medium between the user and the Connectionist Expert Diagnosis System. The User Interface requires the experienced operators to input the data set of the elemental samples in an interactive manner. The fault situations to be considered, number of possible faults, codes of these faults and their respective primary/back-up protective relays and breakers are input sequentially. To create the training samples, the User Interface automatically compiles the elemental samples into different combinations of the faults and their Corresponding states of relays and breakers, possible failure devices and their back-up protection.

These training samples are separated into three subsets of data: Subset I is the symptom vectors comprising the states of relays and breakers; Subset I1 is the corresponding fault codes; Subset 111 is the associated failure relays or breakers and their back-up protection if existing. The mapping of these three subsets of data is schematically described in Fig. 2. As shown in this figure, the states of relays and breakers are expressed as binary values of "1" or "0", with "1" standing for "operate" or "opened", and "0" for "not operate" or "closed".

When the configuration or protective scheme of the substation changes, only the related elemental samples to the portion changed need be input into the system interactively. The User Interface will then modify the training samples by itself.

Subset I Subset I1

Symptom Pattern Data Fault Code

The models of the substation and the distribution network are also input into the system through the User Interface. Based on these models, the EMTP simulates all possible faults for given fault impedances and loading conditions and generates the training samples needed.

3-4 Connectionist-tvue Knowledge Base

The knowledge base of our Connectionist Expert Diagnosis System can be automatically constructed according to the data sets from the User Interface. The structure of the Connectionist- type Knowledge Base for the fault diagnosis is illustrated in Fig. 3. In this figure, the data flow between the connectionist modules of the knowledge base is also depicted.

The knowledge base consists of three hierarchical modules: Forward Diagnosis Connectionists (FDCs), BackTracking Con- nectionists (BTCs) and Comment Connectionists (CCs). The FDCs receive the current states of protective relays and breakers, and output the potential faults. The architecture of FDCs consists of several sub-connectionists, to each of which one fault class is assigned. One fault class here refers to a particular fault situation, such as, (1) Single fault and no failure protective device, (2) Single fault and one failure relay, (3) Single fault and one failure breaker, (4) Single fault and two failure breakers, etc. Each of these sub-FDCs is termed as FDCl, FDC2, FDC3, ..., etc., as shown in Fig. 3.

Subsets of the training sample data from the User Interface, symptom vectors (Subset I) and corresponding fault codes (Sub- set 11), are used as the input and output (or teacher) signals, respectively, to train and implement the FDCs. In the cases where the same symptom pattern maps several probable situations of single fault, the trained FDCs are prone to announcing multiple faults a t the same time (e.g., the FDC2). However, this result

Subset 111

Failure Primary Back-up Secondary Back-np Device Device Device

326

I , I I I I I ...

Hidden layer

NoC ( I ) The oulpul of failure dence II linked Lo tb back-up protectmon denceo (2) One of the r u b - m p (Ihr W O ~ F I ) ~ 1 - output to Ihe CCI

Fig. 3: Hierarchical Architecture of the Connectionist-type Knowledge Base

mis-informs the real situation. To solve the problem, a particular flag node is added into the sub-connectionist. The output of the flag designates the curreut situation is one of several probable single faults (with ”flag one”) or multiple faults at the same time (with ”flag zero”). This additional flag node is thus called an ”Exclusive OR” (”XOR”) node. In such cases of ”flag one”, the ADCM is resorted to helping estimate the real fault situation.

The error back-propagation learning algorithm [ll] is used to iudependently and alternately train the sub-FDCs. Only the connection weights pertinent to the individual sub-FDC under training are subject to chauge and the other connection weights are left unchanged.

The organizatiou of the BTCs is analogous to that of the FDCs, except that the training signal flows are reversed and the sub-BTC has its own independent input and output layers. Videlicet, the BTCs are inverse mapping networks of the FDCs, the former representing the mapping from the fault space to the symptom space, while the latter from the symptom spare to the fault space.

The CCs are organized to relate the faults to their primary protective devices if no relay or breaker fails (CC1). Whereas, if there exists failure relay or breaker, the CCs associate the fault and its symptom pattern with the failure relay or breaker. Therefore, in the training phase of the CCs, the input is the training samples in Subset 1 (symptom patterns) and Subset I1 (fault codes), and the output teacher signal is the failure relay or breaker i n Subset 111. The identified failure relay or breaker is then linked to its back-up protection devices. The training algorithm of BTCs and CCs is the same as that of FDCs. A single connectionist training program developed can thus be used to train all the three connectionists as well as the ADCM described helow.

3-5 Auxiliarv Diaenosis Connectionists Model

The ADCM is designed as a hierarchical two-level structure of connectionists model, as shown in Fig. 1. In the first level is a distributed processing structure. The architecture of this level consists of several connectionists (denoted by ADC1, ADCZ, ..., ADCm, etc.), each of which is responsible for the fault diagnosis at a particular section. A section of the substation here is meant by a feeder, a bus or a transformer, which can be separated by breakers.

The connectiouists receive the peak values of bus voltages and feeder curreuts a i d guess the potential fault at the section. Each connectionist contains 7 input nodes for 3 phase bus voltages, 3 phase feeder currents and 1 neutral line current, and 13 output nodes for 13 kinds of faults concerned in this paper. Having their own structures and training samples, the connectionists can also be trained independently by using the error back-propagation learning algorithm. Besides, when any configuration of the substation changes, only the related connectionist needs re-training, while the weights of the other ADC’s remaining the same. Due to the modular structure, the ADCM can adapt flexibly to the changes of the substation configuration.

The second level of the ADCM is the Synthesis Unit. Ac- cording to the output of each ADC, the Synthesis Unit provides the final diagnosis result of the ADCM. If there are more than one ADC’s announcing the existence of faults, the Synthesis Unit integrates and compares each fault announced. For example, the faults occurring simultaneously at a feeder aud its back-up protection zone are regarded as a single fault at the feeder only.

3-6 Inference Ennine

With respect to current input symptom pattern, the Inference Engine determines one of the possible fault classes presented by

321

used to give the information of primary protective devices (from CCl), failure relay or breaker and its back-up protection (from the appropriate sub-CC).

3-7 D a t a Prepara t ion Uni t

The Data Preparation Unit receives tlie status signals of protective relays and breakers as well as the bus voltages aud feeder currents from the SCADA (Supervisory Control And Data Acqui- sition) system. These signals are integrated in the form of symptom data and then provided to the Connectionist-type Knowledge Base for diagnosis.

4. Practical Svs tem Test

4-1 Test Svs tem Description

The practical Taipower secondary substation utilized to test the effectiveness of the proposed Connectionist Expert Diagno- sis System is shown in Fig. 4. The substation consists of four sub-transmission lines, two main transformers, two shunt capacitors, one tie-line circuit breaker, one primary bus bar (with line voltage of 69kV) and two secondary bus bars (with line voltage of 11.4kV). Each secondary bus contains five radial distribution feeders. The distribution feeders in the system are protected by over current (CO) relays, low energy over current (LCO) relays and reclosers (RC). The main transformer is protected by differential relay (DR), and shunt capacitors by CO, LCO and undervoltage (UV) relays. The buses are protected by CO and LCO relays. The breakers of the bus bar are the back-up protection for each feeder breaker, where coordination time delay exists between the primary and the back-up protection devices.

4-2 Svstem Implementation

The Connectionist Expert Diagnosis System has been implemented on a personal computer. It is written in Turbo-C lan- guage. A PC-emulator of SCADA system is also developed to test the SCADA interface in the Data Preparation Unit. The User In- terface receives the elemental samples by interactively requiring the operator to key in the information needed. After acquiring the necessary information, the User Interface compiles these data and initiates the construction of the Connectionist-type Knowl- edge Base. The models of the substation and the distribution network are also provided to the User Interface as the input data of the EMTP.

Training of t h e Diaanosis Svs tem

According to the data provided by the operator, the test substation has 17 breakers, 86 relays, 176 possible fault types and 4 fault classes. Consequently, four sub-FDCs sharing 103 input nodes are automatically set up. FDCl has 27 output nodes and the other sub-FDCs have 23 output nodes. Total 474 training samples for FDC1, 176 samples for FDC2, 420 samples for FDC3 and 380 samples for FDC4 are obtained from the elemental samples after compiling by the User Interface.

The training samples of ADCM are created by running the EMTP. Based on the configuration of the test substation, total 17 connectionists (ADC1, ADC2, ..., ADC17) are assigned to the 17 fault sections concerned in the substation. According to different combinations of fault impedances and loading conditions, the training set for each connectionist is composed of 1,935 samples.

Detailed composition of these training samples for individual connectionist modules and their sub-counectionists is listed in Table 2. This table also describes the architecture of each connectionist. Test of t h e Diaanosis Sys tem

each sub-FDC and the ADCM. The decision is based on the cal- culation of confidence level for each possible fault class. The In- ference Engine is then attempted to explain how the conclusion is drawn in terms of the association of operating (and failure) relays and breakers with the fault diagnosed. The peak values of bus voltages and feeder currents at the fault section are also displayed for cross reference.

Referring to the hierarchical organization of the Connectionist- type Knowledge Base shown in Fig. 3, the same input nodes of sub-FDCs receive the state values of relays and breakers. Af- ter processing, the individual sub-FDCs, FDC1, FDCB, ..., etc., answer their own faults diagnosed. One of the fault situations should be determined as the final conclusion of the system. As described in the Connectionist-type Knowledge Base, the input- output mapping of the BTC is the inverse of its FDC. For a fault situation correctly identified, the input of sub-FDC must be iden- tical to the output of the related sub-BTC, if no error or missing data exist. By comparing the input of the sub-FDC with the output of the corresponding sub-BTC, the confidence level of the sub-FDC answer can be obtained. The confidence level of the answer is calculated by the following heuristic equation:

where h is the Hamming error of the sub-FDC input symptom pattern and the sub-BTC output symptom pattern. In ( l ) , Ham- ming error is defined as the number of mismatched bits for two binary values. By comparing the confidence level of each sub- FDC, the answer of the winner sub-FDC (with the largest confidence value) is selected as the final conclusion. The associated sub-BTC is the winner sub-BTC.

The output pattern of the winner sub-BTC is called "template symptom pattern", which means that it is the most possible cause of the fault diagnosed. By comparing the current symptom pattern of the FDCs with the "template symptom pattern", we could detect the mismatched bits. The mismatched bits may be due to transmission error or missing data. In this way, the Inference Engine is designed to have the capability of error checking.

In the diagnosis process, if the confidence level of the winner sub-FDC is higher than 83.5%, the Inference Engine would take the result of the FDCs as the final conclusion and ignore the answer of the ADCM. However, when the confidence level is lower than 83.5%, the output of the ADCM would he employed as the final conclusion.

In the cases where a definite answer out of several possibilities (i.e., the "XOR" flag is "1") cannot be given simply from the open/close states of the relays and breakers, the answer of the ADCM will be compared with all the possibilities pointed out by the FDCs. If the answer of the ADCM matches one of the sub- FDCs, it will be adopted as the final conclusion of the system. Otherwise, only the possible fault situations offered by the FDCs are given by the system. In the special case of line-open fault which cannot be detected by the relays in the substation, the answer of the ADCM also yields the conclusion of the system.

After the final diagnosis result has been obtained, the Irifer- ence Engine is aimed to explain how the conclusion is achieved. To furnish the Connectionist Expert Diagnosis System with the capability of explanation, the CCs aforementioned are relied on. As long as the winner of the sub-FDCs is determined in the In- ference Engine, the information of the final fault diagnosed and its template symptom pattern is passed to the CCs. When the winner is the FDCl (no failure relay or breaker), only the corresponding CC1 is invoked to obtain the primary protective relays and breakers of the fault. Whereas, if the winner is the other sub- FDCs (i.e., failure operation of relay or breaker is detected), both CC1 and the associated sub-CC (with the winner sub-FDC) are

328

Fig. 4: Circuit Layout of the Practical Taipower Secondary Substation

Table 2: Composition of Training SamDles

Table 3: Composition of Testing Cases

329

Fig. 7(b) is the result. The fault situation is denoted. The primary protective scheme, failure relay, and back-up relays and breakers are given. Confidence level of the collclusion is 100%.

Fault Section

Fault Type

To test the effectiveness of the overall Connectionist Expert Diagnosis System, a set of testing data is created. The testing set contains 250 testing cases, 150 cases of which are randomly selected from the training set, aud the other 100 testing ca.ses are created outside the training set. The 100 testing cases include those with double faults, line-open fault, one-bit transmission error or one missing bit in the symptom patterns.

The testing results show that the proposed diagnosis system can achieve an estimation accuracy as high as 100% both for 150 training samples and for 100 testing samples not contained in the training set. Refer to Table 3 for details of the testing results.

In the above case, the processing time from fault alarm to displaying the fault condition takes less than 1 second, which makes feasible the diagnosis system on-line application. All times are measured at the PC-486 computer.

4-3 Case Studv

From the test cases on the substation, four examples are to be further demonstrated. No transmission errors are assumed in these examples.

A single line-to-ground fault occurs at phase A of feeder 1. CO relay "C03A" operates and trips breaker "CB3", but the LCO relay "LC03" fails. The fault situation is shown in Fig. 5(a), which is a simplified circuit diagram of the substation in Fig. 4.

If a lineto-line fault occurs at phases A and B of feeder 1 but its primary CO relay "C03B" fails, or a line-to-line fault occurs at phases A and C of feeder 1 but its primary CO relay "C03c" fails, the same fault symptom pattern will be obtained in the diagnosis system.

For this case, the FDCs indicate that one of the three faults mentioned above is possible ( " X O R flag is "1") with equal confidence level (100%). The Inference Engine thus takes the result of ADCR4, which estimates the fault situation based on the bus voltages and feeder currents. Accordingly, the real fault situation that a single line-to-ground fault occurs at phase A of feeder 1 and the LCO relay "LCO3" fails is detected. The final result on a computer terminal is shown in Fig. 5(b), where the corresponding peak bus voltages and feeder currents at the fault are appended for reference.

The case is an example of double faults at the same time. There is a line-to-line fault occurring at phases B and C of feeder 1. The CO relays "C03B" and "C03C" operate and trip breaker "CB3". Meanwliile, a single line-to-ground fault occurs at phase A of feeder S, and the CO relay"C013C" and LCO relay "LC013" operate and t,rip breaker "CB13". The fault situation is shown in Fig. 6(a).

Fig. 6(b) is the result of the Connectionist Expert Diagnosis System. These two faults occurring siiiiultaneously are identified together with t,heir fault sectious aiid fault types. The respective primary protective devices and the bus voltages and feeder currents are given for the explanation of this conclusion. Also, the confidence level of the answer (100%) is accompanied.

Case 3 is a single line-to-ground fault which occurs at phase A of feeder 5 . The CO relay "C07A" aiid LCO relay "LC07" operate, but the primary circuit breaker "CB7" fails. The bacb- u p protective relays "C02A" aud "LCO2" thus operate and trip the breaker "CB2", which results in a large area of blackout. The fault situation is shown in Fig, 7(a).

~~

Phaae A of feeder 1 (11.4 kV)

Single linck-ground fault

L i L 2 L 3 L I

69 LV

Operating Protective

Devices

# I TR

#y l Bus 11.4 kV

Primary Protection

Relays CO3A

~ r ~ a k ~ ~ I CB3 Back-up Protection

Relays none

Breaker I none

: Opcned Breaker 0 Operating Rplay D. Failure Rpls)

0 : Closed BE&U x : Trlppcd B r e a k

: Opelid S w e b 0 : Current Tr. : Failure Breaker

X : Fault 0 . Breaker Svmber

Q : Cloaed Switch

Fig. 5(a): Fault Situation of Case 1

I Fault Case 1

Failure Rdaya

Devices Breaker Protective

Confidence

8,923 V Peak Valua of A Bus Voltage Phase 9$468 v

Phase C 9,843 v at (#1 Bus)

Ph- A 1 Peak values of -1 Feeder Current at (Feeder 1) Phase C

I Neutral line I 25 A

Fig. 5(b): Fault Diagnosis Result of Case 1

330

: Opened Breaker 0: Operlling &lay a: F d u r e h i a y

: Closed Brerker I[ : Tripped Bipilor B : C l o d Switch

: Opened Switch 0 : Currcnt Ti : Failure Breakex

X . Fault 0 : Breaker Kumber


Fault Case 1

Fault Type

Explanation Primary Protection

Relays (1) C03B.CO3C; (2) CO13AJXOI3

Operating ~ ~ ~ a k ~ ~ (11 CES; (1) CBI3 Protective

Devices Back-up Protection Relays

Breaiker none

Peak Values of Feeder Current


: Opened Breder Operaring h i a y a. F.uiure R d q

0 : Closed Breaker M : Tripped Breaker 0 Cloaed Swatch

. Opeiied Sv i t i b 0 . Current Tr. 0 Fuluro Breakel

X : Fads o : n r d l r huinbrl


P h e A of feeder 5 (11.4 kV) Fault Section

I


Relays

Back-up Protection Protective Devices

Relays COZA.LCO2


In this case, a line-open fault occurs at phase A of feeder 1. Since this kind of fault caniiot be detected by CO or LCO relay, all the relays and breakers existing in this substatioi~ would not operate or be tripped. Therefore, the FDCs in the Connectionist- type Knowledge Base cannot be relied on to identify the fault.

However, by monitoring the change of the feeder currents, the ADCM detects the fault. The detailed result is shown in Fig. 8.

Phsss A of fader 1 (11.4 kV) Fault Section


none

B d - u p Protection Devices

Fig. 8: Fault Diagnosis Result of Case 4

5. Conclusions

A new structure of connectionist expert system for on-line fault diagnosis has been proposed and demonstrated. The proposed system can be easily established by collecting the elemental samples. These elemental samples associate the faults with their protective relays and breakers by inquiring an experienced operator and with the bus voltages and feeder currents by running EMTP. Decision of the diagnosis system can be reasonably explained by displaying the operating and failure protective devices. Also, the confidence level of the answer is given to account for its credibility. Preliminary results obtained from the test on a practical substation validate the proposed system for the cases contained in the training set as well as for the new testing cases.

For gradually automated substations of the Taipower system, the signals of relays and breakers as well as the bus voltages and feeder currents will be on-line available. The proposed diagnosis system is of much benefit to the operator in estimating the possible fault situation or simply as a double-check for the operator’s decision. Based on the rapid identification of the fault situation by the diagnosis system, the first step of the substation restoration can thus be efficiently accomplished. Most importantly, our system can be easily implemented for various substations with little additional effort.

6. Acknowledvement

This work reported in this paper is sponsored by the National Science Council, R.O.C. under Contract NSC 81-0404-E-006-571. The authors are also greatly indebted to the operators of the Taipower Chi-Shan Substation for supporting the test of the diagnosis system.

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K. Tomsovic, C. C. Liu, P. Ackerman and S. Pope, ”An [2]

33 1

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[3] T. K. MA, C. C. Liu, M. S. Tsai, R. Rogers, S. L. Muchlinski and J. Dodge, ”Operational Experience and Maintenance of On-Line Expert System for Customer Restoration and Fault Testing,” IEEE Trans. on PWRS, Vol. 7, NO. 2, pp. 835.842, 1992. C. Fukui and J. Kawakami, ”An Expert System for Fault Section Estimation Using Information from Protective Re- lays and Circuit Breakers,” IEEE Trans. on PWRD, Vol. 1, No. 4, pp. 83-90, 19S6.

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Hone-Tzer Yang received the B.S. and M.S. degrees in electrical engineering from National Cheng-Kung University,Tainan, Taiwan in 1982 and 1984, respectively. He received his Ph.D. degree in Electrical Engineering from National Tsing-Hua Uni- versity, Hsin-Chu, Taiwan in 1989. He is a Senior Specialist of Chung Shan Institute of Science and Technology since 1989. He is also an associate professor of Electrical Engineering Department at National Cheng-Kung University.

His present research interests are neural networks and expert system applications, signal processing and load forecasting in power system. Dr. Yang is a member of Phi Tau Phi and the IEEE PES.

Wen-Yeau Chang received the B.S. degree in electrical engineering from National Taiwan Institute of Technology, Taipei, and the M.S. degree in electrical engineering from National Tsing- Hua University, Hsin-Chu, in 19S6 and 19S8, respectively.

He is currently working toward his Ph.D. degree at National Cheng-Kung University, Tainan, Taiwan. His research interests are on the restoration and fault diagnosis of power system.

China-Lien Huang received B.S. degree in electrical engineering from National Cheng-Ihng University, Tainan, Taiwan, in 1957, and R?. S. E. E. degree from Osaka University, Osaka, Japan, in 1973. Since 1964, he has been with the Department of Electrical Engineering, National Cheng-Kung University, where he is now a professor. His major interests are high voltage engineering, power system switching surge and protection.

Documents

On-line fault diagnosis of power substation using connectionist expert system