The Transformer Fault Portfolio Diagnosis Based On The Multiple Bayesian Classifier

With the development of domestic power industry, the data of transformer condition proliferate, and new methods in virtue of intelligent technique are called for transformer fault diagnosis. Due to the information of test data is incomplete and deviated in the power transformer fault diagnosis, and the Bayesian network can deal with uncertainty well. This article discusses the NB, SB, TAN, BAN and GBN, the five Bayesian classifier models for transformer fault diagnosis, and it is proposed a new method that the combination of the multiple Bayesian network classifiers and SVM for transformer fault diagnosis. The experiments show the portfolio model is more suitable for transformer fault diagnosis, with a capacity processing the lack of information and more fault-tolerant performance, its performance is superior to single classifier method of diagnosis.