Model study of transformer fault diagnosis based on principal component analysis and neural network

Models of transformer fault diagnosis were developed by using on-line data to improve the conventional testing method and physical law methods. The operation data of 7 variables that affect transformer fault had been studied by using principal component analysis method, 5 principal components had been obtained and the contributions of the principal components had been computed. Based on the factors, a three-layer RBF neural network is designed. It is proved by MATLAB experiment that RBF neural network is a strong classifier which can be used to diagnose transformer fault effectively.