Partial discharge classification using neural networks and statistical parameters

Authors:
Hung-Cheng Chen;Po-Hung Chen;Meng-Hui Wang
Affiliations:
Department of Electrical Engineering, National Chin-Yi University of Technology, Taiping, Taichung, Taiwan;Department of Electrical Engineering, Saint John's University, Tamsui, Taipei, Taiwan;Department of Electrical Engineering, National Chin-Yi University of Technology, Taiping, Taichung, Taiwan
Venue:
IMCAS'07 Proceedings of the 6th WSEAS International Conference on Instrumentation, Measurement, Circuits and Systems
Year:
2007

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Abstract

Partial discharge (PD) pattern recognition is an important tool in high-voltage insulation diagnosis of power systems. A PD pattern classification approach of high-voltage power transformers based on a neural network is proposed in this paper. A commercial PD detector is firstly used to measure the 3-D PD patterns of epoxy resin power transformers. Then, the gray intensity histogram extracted from the raw 3-D PD patterns are statistically analyzed for the neural-network-based (NN-based) classification system. The system can quickly and stably learn to categorize input patterns and permit adaptive processes to access significant new information. To demonstrate the effectiveness of the proposed method, the classification ability is investigated on 120 sets of field tested PD patterns of epoxy resin power transformers. Different types of PD within power transformers are identified with rather encouraged results.