Intelligent Fault Diagnosis Method And Its Application

This paper researches the applications of neural network, grey relation degree theory, genetic algorithm and fuzzy optimum selecting theory in transformer fault diagnosis. These intelligent algorithms used in this paper have improved the transformer fault identifying rate greatly.Normal BP algorithm is usually used in fault diagnosis based neural network. In order to improve the learning rate of backpropagation neural network, Levenberg-Marquart algorithm was proposed to learning network optimized weight parameters. Its training speed and stability are higher than the normal BP algorithm. Levenberg-Marquart algorithm is used in transformer online fault diagnosis, acquiring satisfied result.In order to review relation grade of system factors, the grey relation degree is used to quantifying analyse system dynamic development process. It estimates relation degree between factors according to curves analogue degree.3 ration method is usually used for transformer fault diagnosis, it sometimes can not judge fault because of its lack of enough ration codes. Grey relation degree may make up the disadvantage in some extent.Genetic algorithm is a global optimization algorithm imitating biology evolution process. It provides a currency frame of resolving complicated system optimization problem. A fuzzy probability reasoning model for transformer faults diagnosis is rebuilded based on probability reasoning and fuzzy theory. A GA resolvent for the model is put forward from the point of nonlinear combinatorial optimization view. The result of simulation shows the new method has improved the identifying rate of single and multi faults.According to the relative membership degree of normal fault mode toneeding checking mode, fuzzy optimum selecting theory is applicationto transformer fault diagnosis. It may judge fault more explicit than fuzzy even weight model. The result of simulation shows the method may diagnose not only these faults that cann t be diagnosed by using 3 ratio method but also judge fault more explicit. The model may analyze oriently to faults partly, that is a guidance to servicing.