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英文翻译题目:通过分析变压器中溶解气体而进行故障诊断的专家系统姓名: 宋日成学院: 工学院专业: 自动化班级: 自动化112 班学号: 32211218指导教师: 陆静职称: 讲师2015年3月14日南京农业大学教务处制An Expert System for Transformer Fault Diagnosis UsingDissolved Gas AnalysisW. S. Chan ·Y. L. Xu ·X. L. Ding ·W. J. DaiReceived: 9 November 2005 / Accepted: 11 August 2006 / Published online: 7 September 2006© Springer-Verlag 2006Abstract In order to automate the transformer fault diagnosis, improve the accuracy of judgment, the introduction of artificial intelligence expert system fault diagnosis. The system is based on fuzzy reasoning confidence by observing the information, the use of knowledge to reach a conclusion. Test proved this method reduces the randomness of judgment, improved diagnosis.Keywords:Expert System Transformer Troubleshooting1 IntroductionThe 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. Many researchers and electrical utilities have reported on their experience and developed interpretative criteria on the basis of DGA. However, criteria tend 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. For this reason, knowledge-based programming is a suitable approach to implement in such a diagnostic problem. 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, data bases adopted from TPCS 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. 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), is used in the development of the knowledge systembecause, it has excellent man-machine interface that provides suggestions. Moreover, its inference strategy is similar to the MYCIN. 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) andgoal-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 personals to increase the performance of transformer diagnosis.2.DEVELOPMENT OF DIAGNOSIS AND INTERPRETATIONLike 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, 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 oractive 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 concerned with the following representative combustible gases; hydrogen(H2), methane(C2H 2), ethane(C2H6), ethylene(C2H4), acetylene(C2H2) and 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 power transformers to construct its criteria. The developed knowledge base in this paper is partially based on these data. On the other hand, Dornerburg developed a method to judge different faults by rating pairs of concentrations of gases, e.g., CH /H , C H /C2H4, with approximately equal solubility and fusion coefficients. Rogers 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 . 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. Infect, 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 arcing fault 4. quantities of CH 2aid C2H4 2 may be a symptom of an arcing fault.3.THE PROPOSED DIAGNOSTIC EXPERT SYSTEMThis 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 MethodDiagnosis 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 ProcedureThe 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. Some operational limitations of the gas 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 been developed mainlyfrom afree-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' cactive 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 concerned with the following representative combustible gases; hydrogen(H2), methane(C2H 2), ethane(C2H6), ethylene(C2H4), acetylene(C2H2) and 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 normsfrom a sufficiently large sampling to assess the statistics. TPC investigated gas data from power transformers to construct its criteria. The developed knowledge base in this paper is partially based on these data. On the other hanondition. 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 MethodThe 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. 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 characteristicsof a case transformer. Section 3 . 4 gives some details of these rules. 3.2 Expert System Structure The proposed diagnostic expert system is composed of four components, working memory, a knowledge base, an inference engine and aman-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 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 entirerule is true or false. Taking the advantages of these two approaches in the building and structuring of a knowledge base improves inference efficiency significantly. 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.4. IMPLEMENTATION OF THE PROPOSED EXPERT SYSTEMAn 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 are 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 Justification 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. 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. If more information or new techniques support other uncertain membership functions, they can be added into the knowledge bas 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.5.CONCLUSIONSA 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. 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.通过分析变压器中溶解气体而进行故障诊断的专家系统W. S. Chan ·Y. L. Xu ·X. L. Ding ·W. J. DaiReceived: 9 November 2005 / Accepted: 11 August 2006 / Published online: 7September 2006© Springer-Verlag 2006摘要：为了实现变压器故障诊断的自动化，提高判断的准确性，把人工智能专家系统引入到故障诊断中。