电气专业英文文献

An Expert System for Transformer Fault Diagnosis Using Dissolved Gas Analysis

1. INTRODUCTION

The 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 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 oncipient 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. 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 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 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 system because, 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) 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 personals to increase the performance of transformer diagnosis.