电气专业英文文献

1、精选优质文档-倾情为你奉上An Expert System for Transformer Fault Diagnosis Using Dissolved Gas Analysis 1. INTRODUCTIONThe power transformer is a major apparatus in a power system, and its correct functioning its vital to minimize system outages, many devices have evolved to monitor the serviceability of power t

2、ransformers. 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 val

3、uable. 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 oncipient faults. Many researchers and electrical utilities have reported on their experience and developed

4、interpretative criteria on the basis of DGA. However, criteria tend to vary from utility to utility. 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 interpreta

5、tion 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

6、 the synthetic expertise method. Furthermore, data bases 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. This expert system is constructed with rule base

7、d 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 system because, it has excellent man-machine interface that provides suggestions. Moreover,its inference strategy is simila

8、r 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. Wit

9、h 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 fea

10、sibility 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 A

11、ND INTERPRETATION Like many diagnostic problems, diagnosis of 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 Japanes

12、e 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 general

13、ly produced by oil degradation and other insulating material, 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

14、 gas chromatography to analyse 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(C2H2), ethane(C2H6), ethylene(C2H2) and carbon monoxide(C

15、0).Many interpretative methods based on DGA to 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

16、 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 hand, Dornerburg developed a method to judge different faults by rating pairs of concentrations of gases, e.g., CH/H, GH/C3H4,

17、 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 choic

18、e 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 distinguis

19、h three basic fault processes:overheating(pyrolysis), corona(partial dischatge) and arcing discharge. Corona and arcing arise from electrical faults, while overheating is a thermal fault. Both types of faults my lead to deterioration, while damage from overheating is typically less than that from el

20、ectrical stress. Infect, different gas trends lead to different faulty types, the key gas method is identified. For example, large amounts of CH and H are produced with minor arcing fault 4 quantities of CH 2aid C2H2 may be a symptom of an arcing fault.3. THE PROPOSED DIAGNOSTIC EXPERT SYSTEM This s

21、tudy 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 f

22、ew 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 cor

23、e 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 ins

24、ulation involving CO and CO are handled separately and the gas ratio codes have been 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 l

25、imitations. 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 dega

26、ssed time must be known for further inference. Norms(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 diagno

27、sis 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 analys

28、is 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 co

29、nventional gas ratio and key gas method.Obviously, the above gas trends should be incorporated with other evidences under the experienced procedure for practical use. Norms threshold, the gassing rate, the quantity of total combustible gas(TCG), the TPC maintenance expertise and the fuzzy set assign

30、ment are all important evidences considered in the synthetic diagnosis.Other expertise based on a transformer historical data base is also used to analyse the characteristics of a case transformer. Section 3.4 gives some details of these rules.3.2 Expert System StructureThe proposed diagnostic exper

31、t system is composed of 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 curren

32、t gas concentration, some are from the user, others are retrieved from the transformer's historical data base. Note that the fuzzy set concept is incorporated to create fuzzy variables on the request of system reasoning. A knowledge relationship, which uses these facts, as the basis for decision

33、 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 heur

34、istic 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(

35、data-driven) and backward chaining(goal-driven). Fuzzy rules, norms rules, gas ratio rules, synthetic expertise rules and some of the maintenance rules and some maintenance rules, use forward chaining.As for the searching strategy in KES, the depth first searching and short-circuit evaluation are ad

36、opted. 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.

37、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 h

38、elp of this interface, the capability of tracing, explaining and training in an expert system is greatly simplified.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

39、 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 t

40、heir transformer oil samples periodically. After analysing oil samples, more than ten years' worthy gas records are collected and classified into three voltage level, 69KV, 16KV and 345KV. Thus, gas records for one transformer are composed of several groups of data. In the process of DGA interpr

41、etation, 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 pm in volume concentration. 100 pm is equal to 0.01 ml(gas)/100ml(oil).From the expertise of diagn

42、osis, the normal state can be confirmed only by inspection of the transformer's 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 depende

43、nt 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.

44、 Among those implemented, three are listed and demonstrated.Shown in Table 5 are the 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 judgement, a summary of results was obtained. As previously s

45、tated, 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 inform