An integrated method of set pair analysis and association rule for fault diagnosis of power transformers

Abstract

Fault diagnosis of power transformers is crucial to the healthy operation of transformers. In order to enhance its accuracy and reliability, a new fault diagnosis method based on Set Pair Analysis (SPA) and association rules was proposed in this paper. Via analyzing the relationship of fault symptoms and fault types, the corresponding association rules could be established. Via computing the support degrees and confidence degrees of the association rules, the constant weight coefficient of each symptom and the variable weight coefficient of each type could be obtained. The diagnosis method could avoid the subjective defects effectively. Via introducing the concept of subordinate degree, the connection degrees of each fault type and the whole running state of transformer could be obtained. This method could also improve the accuracy of uncertainty factors of transformer fault diagnosis. Experimental results of the test substation proved this method had a higher accuracy by comparing with both association rules and SPA.