IEEE Transactions on Power Delivery, Vol. 8, No. 1, Jariuary 1993. 23 1

An Expert System for Transformer Fault Diagnosis Using Dissolved Gas Analysis

C. E. Lin * J. M. Ling C. L. Huang Senior Member St. Member Member

* Institute of Aeronautics and Astronautics

Cheng Kung University Tainan, Taiwan, China

Department of Electrical Engineering

ABSTRACT -- A prototype of an expert system based on the dissolved gas analysis (DGA) technique for diagnosis of a suspected transformer faults and their maintenance actions is developed. The synthetic method is proposed to assist the popular gas ratio method. Moreover, the uncertainty of key gas analysis, norms threshold and gas ratio boundaries are managed by using a fuzzy set concept. Incorporation of the norms method, the gas ratio method, the synthetic method, a data base of Taiwan Power Company's (TPC) transformer gas records, and TPC's expertise for diagnosis and maintenance increases the performance capability and reduces the operational limitations. This expert system is implemented into PC-AT by using KES with rule-based knowledge representation. The designed expert system has been tested for TPC's transformers gas records to show its effectiveness in transformer diagnosis.

Keywords: Transformer Diagnosis, Dissolved Gas Analysis, Expert System, Fuzzy Set.

1. INTRODUCTION

The power transformer is a major apparatus in a power system, and its correct functioning is vital to system operations. In order to minimize system outages, many devices have evolved to monitor the serviceability of power transformers. These devices, such as, Buchholz relays or differential relays, respond only to a severe power failure requiring immediate removal of the transformer from service, in which case, outages are inevitable. Thus, preventive techniques for early detection faults to avoid outages would be valuable.

In this way, analysis of the mixture of the faulty gases dissolved in insulation oil of power transformer has received worldwide recognition as an effective method for the detection of incipient faults 11.21. Many researchers and electrical utilities [3-91 have reported on their experience and developed interpretative criteria on the basis of DGA. However, criteria tends to vary from utility to utility. Each approach has limitations and none of them has a firm mathematical description. Therefore, transformer diagnosis is still in the heuristic stage [lo]. For this reason, knowledge-based programming is a suitable approach to implement in such a diagnostic problem. Practical uses of knowledge-based expert systems have

92 WM 243-6 PWRD A paper recommended and approved by the IEEE Transformers Committee of the IEEE Power Engineering Society for presentation at the IEEE/PES 1992 Winter Meeting, New York, New York, January 26- 30, 1992. Manuscript submitted July 30, 1991; made available for printing November 25, 1991.

been realized in many areas of the power industry [Ill.

Based on the interpretation of DGA, a prototype of an expert system for diagnosis of suspected transformer faults and their maintenance procedures is proposed. The significant source in this knowledge base is the gas ratio method. Some limitations of this approach are overcome by incorporating the diagnostic procedure and the synthetic expertise method. Furthermore, a data base adopted from TPC'S gas records of transformers are incorporated into the expert system to increase the practical performance. Uncertainty of diagnosis is managed by using fuzzy set concepts [12-141.

This expert system is constructed with rule- based knowledge representation, since it can be expressed by experts. The expert system building tool, Knowledge Engineering System (KES) [151, is used in the development of the knowledge system because, it has excellent man-machine interface that provides suggestions. Moreover, its inference strategy is similar to the MYCIN 1161. a famous rule-based expert system used for medical diagnosis. The uncertainty of human qualitative diagnostic expertise, e.g., key gas analysis, and another quantitative imprecision, such as, norms threshold and gas ratio boundaries etc., are smoothed by appropriate fuzzy models. With the results of such implementation, different certainty factors will be assigned to the corresponding expertise variables. Both event-driven (forward chaining) and goal-driven (backward chaining) inferences are used in the inference engine to improve the inference efficiency.

To demonstrate the feasibility of the proposed expert system, around hundreds of TPC historical gas records have been tested. It is found that more appropriate faulty types and maintenance suggestions can support the maintenance personnels to increase the performance of transformer diagnosis.

2. DEVELOPMENT OF DIAGNOSIS AND INTERPRETATION

Like many diagnostic problems, diagnosis of an oil-immersed power transformer is a skilled task. A transformer may function well externally with monitors, while some incipient deterioration may occur internally to cause a fatal problem in the latter development. According to a Japanese experience [71, nearly 80% of all faults result from incipient deteriorations. Therefore, faults should be identified and avoided at the earliest possible stage by some predictive maintenance technique. DGA is one of the most popular techniques for this problem.

Fault gases in transformers are generally produced by oil degradation and other insulating materials, e.g., cellulose and paper. Theoretically, if an incipient or active fault is present, the individual dissolved gas concentration, gassing rate, total combustible gas (TCG) and cellulose degradation are all significantly increased. By using gas chromatography to analyze the gas dissolved in a transformer's insulating oil, it becomes feasible to judge the incipient fault types. This study is

0885-8977/92/$3.00Q1993 IEEE

232 Table 1. Classification table by Rogers [4].

Range of gas ratio (volume/volume) 'ZH2 CH4 C2H4

'2'4 H2 C2H6

- - L -

0 1 0 . smaller than 0.1 1 0 0 0.1 - 1 1 2 1 1 - 3 2 2 2 greater than 3

Characteristic Fault

0 0 0 Normal aging

* 1 0

1 1 0

1-2 0 1-2

1 0 2

0 0 1

0 2 0

0 2 1

0 2 2

Partial discharges of low energy density Partial discharges of high energy density Discharge of low energy (continuous sparking) Discharge of high energy (with power follow through) Thermal fault of low temp. less than 150 degree C Thermal fault of low temp. between 150-300 degree C Thermal fault of medium temp. between 300-700 degree C Thermal fault of high temp. greater than 700 degree C

* Not significant

concerned with the following representative combustible gases; hydrogen(H2), methane(CH ), ethane(C2H6), ethylene(C2H4), acetylene(C2H2) tnd carbon monoxide(C0).

Many interpretative methods based on DGA to diagnose the nature of incipient deterioration have been reported. Even under normal transformer operational conditions, some of these gases may be formed inside. Thus, it is necessary to build concentration norms from a sufficiently large sampling to assess the statistics. TPC investigated gas data from 291 power transformers to construct its criteria. The developed knowledge base in this paper is partially based on these data [91.

On the other hand, Dornerburg [31 developed a method to judge different faults by rating pairs of concentrations of gases, e.g., CH /H , C H /C2H4. with approximately equal solubilitiei ind &$fusion coefficients. Rogers [41 established mare comprehensive ratio codes to interpret the thermal fault types with theoretical thermodynamic assessments. This gas ratio method was promising because it eliminated the effect of oil volume and simplified the choice of units. Moreover, it systematically classified the diagnosis expertise in a table form. Table 1 displays the ratio method as proposed by Rogers [ 4 1 .

The dissolved gas may vary with the nature and severity of different faults. By analyzing the energy density of faults, it's possible to distinguish three basic fault processes: overheating (pyrolysis), corona (partial discharge) and arcing discharge. Corona and arcing arise from electrical faults, while overheating is a thermal fault. Both types of faults m y lead to deterioration, while damage from overheating is typically less than that from electrical stress. In fact, different gas trends lead to different faulty types, the key gas method is identified.' For example,

large amounts of C H and H are produced with minor

4 arcing fault [ 5 , 8 ] .

quantities of CH 2aid C2H4 2 may be a symptom of an

3. THE PROPOSED DIAGNOSTIC EXPERT SYSTEM

This study is aimed at developing a rule-based expert system to perform transformer diagnosis similar to a human expert. The details of system processing are described below.

3.1 The Proposed Diagnostic Method

Diagnosis is a task that requires experience. It is unwise to determine an approach from only a few investigations. Therefore, this study uses the synthetic expertise method with the experienced procedure to assist the popular gas ratio method and complete practical performance.

3.1.1 Experienced Diagnostic Procedure

As shown in Figure 1. the overall procedure of routine maintenance for transformers is listed. The core of this procedure is based on the implementation of the DGA technique. The gas ratio method is the significant knowledge source. The Dornerburg 131. Rogers and IEC 141 aVDDrOaCheS have been implemented - - together. Some operational limitations of the

Gi+ I relevant inforration I

1

T Figure 1. Procedure of transformer cli.agnostic

expert system.

233

ratio method exist. The ratio table is unable to cover all possible cases. Minimum levels of gases must be present. The solid insulation involving CO and CO are handled separately and the gas ratio codes have &en developed mainly from a free-breathing transformer. Other diagnostic expertise should be used to assist this method. Norms, synthetic expertise method and data base records have been incorporated to complete these limitations.

The first step of this diagnostic procedure begins by asking DGA for an oil sample to be tested. More important relevant information about the transformer's condition, such as the voltage level, the preservative type, the on-line-tap-changer (OLTC) state, the operating period and degassed time must be known for further inference. Noms (criteria) Set up by TPC power transformers' gas characteristic data are then used to judge the transformers' condition. For the abnormal cases, the gas ratio method is used to diagnose transformer fault type. If different or unknown diagnosis results are found from these ratio methods, a further synthetic expertise method is adopted. After these procedures, different severity degrees are assigned to allow appropriate corresponding maintenance suggestions.

3.1.2 Synthetic Expertise Method

The ratio trend, norms threshold, key gas analysis and some expertise are considered as different evidences to confirm some special fault types. In other words, more significant evidences have been collected for some special fault type, better assessment of the transformer status is obtained.

The ratio trend can be seen as a modification of the conventional gas ratio and key gas method. After analyzing the gas ratio method, the ratio of C H to

is found to be remarkably relevant to the2 gault :$$. The ratio of C H to C2H6 is relevant to distinguish the fault teZp$rature. Consideration of these gas trends can predict the fault type. For example,

IF C H and H concentration is high AND tie2concengration of C H is greater than C H 2 6 THEN arcing fault is suggesgiig.

IF C H concentration is trace or low AND tie2concentration of c H is greater than c H AND the concentration of C6,'1s greater than C ii 2 6 THEN high temperature overheatlng is suggesting.

Obviously, the above gas trends should be incorporated with other evidences under the experienced procedure for practical use. Noms threshold, the gassing rate, the quantity of total combustible gas (TCG), the TPC maintenance expertise and the fuzzy set assignment are all important evidences considered in the synthetic diagnosis. Other expertise based on a transformer historical data base is also used to analyze the characteristics of a case transformer. Section 3 . 4 gives some details of these rules.

3.2 Expert System Structure

As shown in Figure 2, the proposed diagnostic expert system is composed of four components, working memory, a knowledge base, an inference engine and a man-machine interface. Working memory (global data base) contains the current data relevant to solve the present problem. In this study, most of the diagnostic variables stored in the data base are current gas concentration, some are from the user, others are retrieved from the transformer's historical

Rule-Based Expert Systen

Know ledge Base

I I Knowledge Relationship

I

Norking Wemory

T T .L

Transformer

I Inference Engine I

Tl Interative Interface I

Knoyledge Acqu is1 t ion I Proper

Requirements Suggest ions User 1

Figure 2 . The structure of the diagnosis expert system.

data base. Note that the fuzzy set concept 1s incorporated to create fuzzy variables on the request of system reasoning.

A knowledge base is the collection of domain expertise. It contains facts and knowledge relationship, which uses these facts, as the basis for decision making. The production rule used in this system is expressed in IF-THEN forms. A successful expert system depends on a high quality knowledge base. For this transformer diagnostic system, the knowledge base incorporates some popular interpretative methods of DGA, synthetic expertise method and heuristic maintenance rules. Section 3.4 will describe this knowledge base.

Another special consideration in the expert system is its inference engine. The inference engine controls the strategies of reasoning and searching for appropriate knowledge. The reasoning strategy employs both forward chaining (data-driven) and backward chaining (goal-driven). Fuzzy rules, norms rules, gas ratio rules, synthetic expertise rules and some of the maintenance rules are implemented by backward chaining. Other rules, such as, procedure rules and some maintenance rules, use forward chaining.

As for the searching strategy in KES, the depth- first searching and short-circuit evaluation are adopted. The former can improve the search efficiency by properly arranging the location of significant rules in the inference procedures. The latter strategy only searches the key conditional statements in the antecedent that are responsible for establishing whether the entire rule is true or false. Taking the advantages of these two approaches in the building and structuring of a knowledge base improves inference efficiency significantly.

234 As for man-machine interface. KES has an

effective interface which is better than typical knowledge programming languages, such as, PROLOG or LISP. With the help of this interface, the capability of tracing, explaining and training in an expert system is greatly simplified.

3.3 Fuzzy Set Application

One of the most important capabilities of a human expert is his ability to deal effectively with imprecise, incomplete and uncertain information. Properly handled uncertain data is crucial to the success of many machine intelligence programs. Fuzzy sets were proposed to manage such linguistic or uncertain information [12-141.

3.3.1 Fuzzy Set Description

An ordinary set can be characterized as a binary function. Elements in the set can be assigned to 1, and the remaining elements of the universe can be assigned to 0. As suggested by Zadeh in 1965 [131, this function was generalized so that the value assigned to the element of the universal set located within a specified range indicated appropriately the membership grade of these elements within the set. Larger values denoted higher degrees of set membership. Such a function is called a membership function, and the corresponding set is a fuzzy set.

Before using the concept of fuzzy set, some definitions will first be described.

DefiIhition 1. Fuzzy Set

Let X be a universal set and x be elements of the universal. Then, a fuzzy set A is an ordered pair:

A = {(x,pA(x))l XE X} , (1)

where l.~ is the membership function, and typically falls within [0,11.

Definition 2. Complement

Let p- be the membership function of the complement o+ a fuzzy set A, then

px(x) = 1 - l I * ( X ) , X E x (2)

Definition 3. Intersection

Let A and B be two fuzzy sets with membership functions pA(x) and pB!x), respectively. The membership function of the intersection c = A n B, is

3.3.2 Fuzzy Set Implementation

Human experts are better at than quantitative predictions.

qualitative judgments However, if an expert

system wants to “absorb” expertise from humans, a difficulty must first be overcome. Change human verbal (or qualitative) judgments into numerical (or quantitative) expressions. Fuzzy set models can facilitate this approach.

Three main diagnostic uncertainties, i.e., norms, gas ratio boundaries and key gas analysis, are managed by the fuzzy set concept in this study. Two basic membership functions are defined before later fuzzy set implementation.

Model 1.

I p H Ascending demi-Cauchy distribution function:

X? A (4)

I l + ( (A-~)/a)~l-l, otherwise

Model 2.

)lL Descending demi-Cauchy distribution function:

I 1 x < A

where A and a are pairs of parameters which are selected to give corresponding proper membership functions. In this diagnosis case, A may be considered as boundaries parameter. However, a is a distribution parameter.

(A) Fuzzy data of norms threshold

The membership function of an abnormal norms threshold p belongs to U . Each of three different voltage levefs and gas typ& have its own experienced parameters which is listed in Table 2. Figure 3 shows the membership function lJ of 69KV for three different gases : C2H4, C H

The membe?shp function of normal norms threshold pn is the complement of It is not shown here because of limited space. a’

and CH

Table 2 . The parameter A and a for abnormal norms

a’ membership function lI

CAS CONCENTR4TION

The membership functionpa for C2H4, C2H6. CH4 gas in 69KV.

Figure 3.

(B) Fuzzy data of gas ratio boundaries

The membership function of gas ratio boundaries is assigned to p . Three ratio codes, ZERO, ONE, TWO, are classified rfor each gas ratio using fuzzy set notation. To demonstration this fuzzy data, ratio C H to C H (r3) is referenced from Table 1. Ozvfously, p:3aZERO) belongs to and p (TWO) belong to p . However, p (ONE) is +he infzrsection of

pS(ZEWO) and p (TWOf! Figure 4 shows this type of membership fungion. Table 3 lists the experienced parameters of IEC gas ratio in this study.

3 3 2 P

ONE TWO 1.2

1.0

0.4 :i 0.0 0.2 0.0 0.6 1 1.0 jl 1.6 2.0 2.6 S.0 9.6 4.0

C2H4/C2H6

Figure 4. The membership Function p €or C2Hq/C2Hg.

(C) Fuzzy data of the key gas analysis

The application of the key gas method in a synthetic expertise method needs to manage the uncertain boundaries of a key gas. The key gas concentration is concerned with fuzzy linguistic variables, like the membership function assignment for ur. The membership functions' parameters of key gas analysis for five different gas concentrations are listed in Table 4. Figure 5 shows the membership function for the C H concentration. Other gas concentration can be iazaged with the same concept.

Like MYCIN [ 161 , the certainty factor is assigned to the corresponding uncertain variable because of many heuristic rules involving fuzzy assignment in the knowledge base.

Q E LOW

235

YED HIGH

h . . . , I 20.0 26.0 SO.0 S6.0 40.0 4S.0 60.0

C2H2 GAS CONCENTRATION

Figure 5. The membership function p k for C2H2 concentration.

a A a A a A a A a A

3.4 The Knowledge Base

The knowledge base of the transformer diagnostic system incorporates the popular interpretative method of DGA, synthetic expertise method and heuristic maintenance rules. Many expertise rules using fuzzy assignment variables exist in the knowledge base.

3.4.1 Heuristic Rules for interpretation of DGA

The problem of extracting and encoding the knowledge of an expert is a basic, but cumbersome task for all expert-system builders. It takes a lot of time to combine systematically all the complementary methods for ratio method. Some heuristic rules for DGA interpretation is shown below:

* IF AND AND THEN

* IF AND

THEN

* IF AND OR AND AND THEN

C H /C H ratio is in fuzzy-interval-0 CB f~ 2ritio is in fuzzy-interval-2 B /i!2~6 ratio is in fuzzy-interval-1

k$ence suggests a thermal fault of medium temperature.

some combustible gas concentrations are high the rate of gas production is oscillating between some levels transformer may be normal if its historical data has the same trend.

voltage level is 69KV TCG concentration is greater than MED gassing rate is HIGH C H gas concentration is high C2H2 gas concentration greater than C H a?c?ng fault is suggesting. 2 6

Inevitably, some of the heuristic rules listed above are effective only when combined with diagnostic

236 procedure. Furthermore, it's worthy to note that abnormal condition is suggested only after tracing the data base of testing transformer. The data base is supported by the Metering and Testing Laboratory (MTL) of TPC. Informations include the transformer number, the sampling date, the individual gas concentration, the gassing rate, the amount of TCG, and the past remedies of transformer.

3.4.2 Heuristic Maintenance Rules

More information than just the fault type is needed for further diagnosis of the power transformers. If the results of DGA show abnormal conditions, a consensus of the diagnostic results is classified into four different degrees of severity: normal, cautious, serious and critical. Based on TPC maintenance expertise, the expert system lists the appropriate maintenance scheduling for retesting the oil, and any other proper actions depending on the severity of the condition.

One of them, including a realization form, are shown below:

Rate less than zero abnormal-period demon5:

WHEN the severity level is cautious THEN the cautious procedures should be

implemented.

This rule is realized in the following form:

WHEN abnormal-maint = start and severity = cautious

if voltage = 69000 then THEN

if rate gt 125.0 then abnor-maintl = investigate .&

retest oil per month. else

if rate gt 45.0 then if analo = overheat then

abnor-maintl = retest oil per month.

else .....

4. IMPLEMENTATION OF THE PROPOSED EXPERT SYSTEM

An expert system is developed based on the proposed interpretative rules and diagnostic procedures of the overall system. To demonstrate the feasibility of this expert system in diagnosis, the gas data supported by MTL of TPC have been tested. In Taiwan, the MTL of TPC performs the DGA and sends the results to all acting divisions relating to power transformers. In return, these acting divisions are requested to collect and supply their transformer oil samples periodically.

After analyzing oil samples, more than ten years' worthy gas records are collected and classified into three voltage levels, 69KV. 161KV and 345KV. Thus, gas records for one transformer is composed of several groups of data. In the process of DGA interpretation, all of these data may be considered, but only the recent data which have significant effects on diagnosis are listed in the later demonstration. In MTL, all gas concentrations are expressed by ppm in volume concentration. 100 ppm is equal to 0.01 ml (gas)/100 ml (oil).

From the expertise of diagnosis, the normal state can be confirmed only by inspection of the transformers' norms level. In practice, most of the transformer oil samples are normal, and this can be

inferred successfully on the early execution of this expert system. However, the Success of an expert system is mainly dependent on the capability of diagnosis for the transformers in question. In the implementation, many gas records which are in abnormal condition are chosen to test the Iustification of this diagnostic system. A total of 101 transformer records have been executed and the results are summarized in Table 5. Among those implemented, three are listed and demonstrated.

Table 5. Summary of test results

Thermal NO. 58 42 47 success 57 38 43 93.9%

Note: NO. = number of groups have been tested

Shown in Table 5 are the test results of 101 units of transformers in three types of remedy: normal, thermal fault and arc fault. After comparing them with the actual state and expert judgment, a summary of results was obtained. As previously stated, one unit of transformer may include many groups of gas data. In evaluation, we depicted some key groups in one unit to justify because some transformers may have different incipient faults during different operational stages. Some mistakes implemented from testing are caused by the remaining oil in the oil sampling container, unstable gas characteristics of the new degassing sample and some obscure gas types.

On the basis of two fuzzy models in eqs. (4) and (5), three membership functions are defined in this diagnostic system. If more information or new techniques support other uncertain membership functions, they can be added into the knowledge base to enlarge the performance of this prototype expert system. Furthermore, the parameters described in Table 2, 3 and 4 are suitable for TPC power transformer. Different regions may be modified if the maintenance personnel find more suitable system parameters.

Example 1. (161KV)

No- --- co2 --- --- --_ H2 ": y$ 1 0 0 4 3 7 3 8 2 0 0 7 5 6 5 3 3 169 56 0 37 42 4 156 45 7 68 77 5 201 102 1071 439 48

1: 02/19/1988 2: 02/22/1989 4: 08/ 12/1990 5: 10/23/ 1990

..............................

Internal examination: the safety valve of OLTC has problem

Results of sample No. 5 implementation: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . This transformer seems to have the following fault(s). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Gas Ratio Method:

Rogers diagnosis is: unknown IEC diagnosis is:

Dornerburg diagnosis is: arc <0.50> arc with power follow through <1.00>

Proposal Diagnosis is: arc <0.96> Severity Level: critical <0.85>

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The recommendation of maintenance are: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

investigate immediately <1.00> oil should be degassed <1.00> retest oil within half a month <1.00>

Example 2. ( 69KV)

NO. co2 CO n2 cn4 c2n6 c2n4 c2n2 TCG rate --- --- --- --- --- ---- ---- _--- ---- ---- 1 1134 0 10 24 37 2 1 10 102 0 2 9 1 1 0 44 87 77 54 4 256 9 3 524 0 32 81 6 9 53 3 238 -2 4 379 0 528 3179 320 3020 2314 9379 154 5 0 0 0 8 0 0 0 8 - 1 7 4 9 6 513 32 0 6 3 4 0 45 6

1: 0 5 / 1 2 / 1 9 8 4 2: 1 0 / 1 6 / 1 9 8 5 3: 0 2 / 1 1 / 1 9 8 7 4: 0 5 / 2 2 / 1 9 8 9 5: 1 0 / 3 0 / 1 9 8 9 6: 0 5 / 1 5 / 1 9 9 0 note: 0 9 / 2 6 / 1 9 8 9 new installations to system

.....................................................

Internal examination: the secondary winding has burnt out

Results of sample No.'4 implementation: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . This transformer seems to have the following fault(s). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Gas Ratio Method:

Rogers diagnosis is: unknown IEC diagnosis is: unknown Dornerburg diagnosis is: arc <0.50> and

overheat <0.50> Proposal Diagnosis is: arc <0.92> Severity Level: critical <0.90>

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The recommendation of maintenance are:

investigate immediately <1.00> oil should be degassed <1.00> retest oil within half a month <1.00>

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Example 3. ( 69KV)

NO. co2 CO n2 cn4 c H 2 6

1 4 5 3 0 8 5 126 4 6 2 538 0 142 118 3 1 3 3429 402 300 4 5 17 4 3432 334 206 42 16 5 3305 389 3091 46 17

1: 0 8 / 1 9 / 1 9 8 9 2: 0 9 / 2 7 / 1 9 8 9 4: 0 2 / 2 3 / 1 9 9 0 5: 0 4 / 1 0 / 1 9 9 0

--- --- --- --- --- ----

..............................

C2H4 C2H2 TCG

224 9 6 577 1 9 3 92 576 1 0 1 225 1091

8 2 221 9 0 1 1 0 1 239 1 1 0 1

- - - - -- -- -- - -

3: 0 1 / 1 5 / 1 9 9 0

rate

l o -1

1 4 0 -146

1 3 1

----

-----

237 Results of sample No. 5 implementation:

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . This transformer seems to have the following fault(s). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Gas Ratio Method:

Rogers diagnosis is:

IEC diagnosis is:

Dornerburg diagnosis is:

Severity Level: serious <0.85>

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The recommendation of maintenance are:

oil should be degassed <1.00> retest oil within one month <1.00>

arc with power follow through <1.00>

arc with power follow through <1.00>

arc <0.50>

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

The first two samples have been internally examined. Diagnostic results agree with the actual faulty type causes and appropriate maintenance is suggested. The third transformer began operation in November 1971. In early August 1989, the unit had suffered an arc tracing fault. After repairing and degassing the transformer's insulating oil, a special gas "fingerprint" of this transformer was formed. As demonstrated, an arcing fault is positively suggested from popular gas ratio methods. However. after inspection of knowledge base and data base, this expert system recommends more appropriate maintenance actions.

5. CONCLUSIONS

A prototype expert system is developed on a personal computer using KES. It can diagnose the incipient faults of the suspected transformers and suggest proper maintenance actions. Fuzzy set concept is used to handle uncertain norms thresholds, gas ratio boundaries and key gas analysis. The synthetic method and diagnostic procedure are proposed to assist the situation which can not be handled properly by the gas ratio methods. Results from the implementation of the expert system shows that the expert system is a useful tool to assist human experts and maintenance engineers.

The knowledge base of this expert system is incorporated within the popular interpretative method of DGA, synthetic expertise and heuristic maintenance rules. The data base supported by TPC MTL for about 10 year collection of transformer inspection data is also used to improve the interpretation of diagnosis. The three examples presented are depicted from records and a summary of test results are listed to justify them. Through the development of the proposed expert system, the expertise of TPC MTL can be reserved. In addition, this work can be continued to expand the knowledge base by adding any new experience, measurement and analysis techniques.

ACKNOWLEDGMENT

This work was in cooperation with the Metering and Testing Laboratory (MTL), Taiwan Power Company (TPC), Taipei. The authors would like to express their sincere gratitude to MTL Engineers for their valuable comments and vital supports. Financial support from the National Science Council of the Republic of China under contract number NSC 80-0404-E006-06 is also appreciated.

238 REFERENCES Chin E. Lin (M 84, SM 90) - Dr. Lin was born on

November 15, 1952, at Chang-Hua, Taiwan. He graduated from Cheng Kung University for ESEE and MSEE in 1975 and 1977 respectively. He received Doctor of Engineering from Lamar University, Beaumont, Texas, in December 1983. He was with Department of Electrical

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Engineering, Cheng Kung University in 1984-1986, after then he is with Institute of Aeronautics and Astronautics, Cheng Kung University. His major interests are control in energy system applications, pulse power system, aircraft control systems, telemetry systems.

Jeeng-Min Ling -- Mr. Ling was born in Tainan, Taiwan on October 28, 1963. He received BSEE and MSEE all from Cheng Kung University in 1985 and 1990, respectively. From September 1990, he started his Ph. D. program in the Department of Electrical Engineering, Cheng Kung University. His present research interests are transformer diagnosis, fuzzy systems and expert system applications..

Ching-Lien Huang -- Professor Huang was born in October 1933 in Tainan, Taiwan. He received B. S. from the Department of Electrical Engineering, Cheng Kung University in 1957, and M. S. E. E. from Osaka University in 1973. He has been with the Department of Electrical Engin- eering, Cheng Kung University since 1964. His major interests are high voltage engineering, energy conversion, power system switching surge and protection.