A Composite Deterministic Model for Transformer Fault Diagnosis and Maintenance Based on Rough Set and Vague Set and Bayesian Optimal Classifier
Based on rough set and vague set and Bayesian optimal classifier, a novel transformer insulation fault diagnosis and maintenance model is proposed in the paper. The method firstly applies fuzzy subjection degree function of the observed information to establish posterior probability of original assumption in Bayesian optimal classifier, the classified results based on each fault information are then calculated, and the best diagnosis result is acquired after all these results are weighted average. Then based on rough model of Bayesian risk decision, the diagnosis results of all fault information are identified to constitute possible maintenance strategies. Actual application shows that the proposed method can deal with the “bottle neck of fuzzy knowledge acquisition in Bayesian optimal classifier and possesses stronger learning abilities, and is a very effective transformer fault diagnosis and maintenance method.
Hongsheng Su
Department of Information and Electrical Engineering Lanzhou Jiaotong University Lanzhou, P.R. China 730070
国际会议
青岛
英文
2006-07-21（万方平台首次上网日期，不代表论文的发表时间）