This paper presents a novel approach based on the back-propagation neural network (BPNN) for the insulation diagnosis of power transformers. Four epoxy-resin power transformers with typical insulation defects are purposely made by a manufacturer. These transformers are used as the experimental models of partial discharge (PD) examination. Then, a precious PD detector is used to measure the 3-D (φ-Q-N) PD signals of these four experimental models in a shielded laboratory. This work has established a database containing 160 sets of 3-D PD patterns. The database is used as the training data to train a BPNN. The training-accomplished neural network can be a good diagnosis system for the practical insulation diagnosis of epoxy-resin power transformers. The proposed BPNN approach is successfully applied to practical power transformers field experiments. Experimental results indicate the attractive properties of the BPNN approach, namely, a high recognition rate and good noise elimination ability. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Chen, P. H., & Chen, H. G. (2007). Application of back-propagation neural network to power transformer insulation diagnosis. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4493 LNCS, pp. 26–34). Springer Verlag. https://doi.org/10.1007/978-3-540-72395-0_4
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