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

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4 Author(s)
Zhu Zhengwei ; Sch. of Inf. Sci. & Eng., Jiangsu Polytech. Univ., Changzhou ; Ma Zhenghua ; Wang Zhenghong ; Jiang Jianming

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.

Published in:

Networking, Sensing and Control, 2009. ICNSC '09. International Conference on

Date of Conference:

26-29 March 2009