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《High Voltage Engineering》 2008-05
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Fuzzy Theory and Probability Reasoning Applied for Identifying Power Transformer Fault

FU Yang,JIANG Yu-rong,CUI Chun-hong,CAO Jia-lin(Shanghai University of Electric Power,Shanghai 200090,China)  
Due to the complexity of electric power transformer fault,an improved fault diagnosis model based on the research theories of electric power transformer fault diagnoses by predecessors was introduced.The new fault diagnosis model is based on the fuzzy theory,probability reasoning and parsimonious covering theory.Not only the dissolved gas in oil analysis and electric tests data were used,but also other observed information was taken into account in the fault diagnosis model.Using the linguistic expression and experience of experts,the relationship between transformer fault characteristics and fault symptoms was built up.Then fault symptoms were filtered by the parsimonious covering set theory.Based on the probability reasoning and parsimonious covering theory,the relative probability function was rebuilt.Not only expert knowledge and experience are imported for the process of transformer fault diagnoses,but also the records of tests and repair history was taken into account in the model.Application of the fault diagnoses system shows that the fault diagnoses model can identify the fault characteristic correctly even with some symptoms absent.There was great superiority for power transformer fault synthetic diagnoses.
【Fund】: 上海市重点科技攻关计划项目(06DZ12204);; 上海市重点学科建设项目(P1301)~~
【CateGory Index】: TM407
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