Application of Back-Propagation Neural Network to Power Transformer Insulation Diagnosis

  • Authors:

  • Po-Hung Chen;Hung-Cheng Chen

  • Affiliations:

  • St. John's University, Department of Electrical Engineering, Taipei, Taiwan, 25135, R.O.C.;National Chin-Yi University of Technology, Department of Electrical Engineering, Taichung, Taiwan, 411, R.O.C.

  • Venue:
  • ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III
  • Year:
  • 2007

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Abstract

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 (茂戮驴-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.