A novel defect classification system of cast-resin transformers by neural network under acoustic emission signal

Authors:
Cheng-Chien Kuo;Teng-Fa Tsao
Affiliations:
Department of Electrical Engineering, Saint John's University, Tamsui, Taipei, Taiwan;Department of Electrical Engineering, Nan Kai Institute of Technology, Tsaotun, Nantou, Taiwan
Venue:
IMCAS'07 Proceedings of the 6th WSEAS International Conference on Instrumentation, Measurement, Circuits and Systems
Year:
2007

Citing 2
Cited 1

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Determination of neural-network topology for partial discharge pulse pattern recognition

IEEE Transactions on Neural Networks

Dynamic programming matching for detecting abnormalities in machines emitting intermittent sounds

AIASABEBI'11 Proceedings of the 11th WSEAS international conference on Applied informatics and communications, and Proceedings of the 4th WSEAS International conference on Biomedical electronics and biomedical informatics, and Proceedings of the international conference on Computational engineering in systems applications

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Abstract

Degraded insulating property of electric equipments will lead to serious accident and great loss for the utilities and customers. Partial discharge detection is an efficient diagnosis method to prevent the failure of electric equipments arising from degrading insulation. However, universal offline partial discharge detection could be performed only during shutdown of equipments. By using the principle of Acoustic Emission (AE) and real-time online detection functions, this paper analyzed partial discharge pattern for cast-resin transformers and conducted high-voltage test of pre-faulty transformers. Furthermore, it collected partial discharge AE signals with selected features and identified their faulty types using artificial neural network. The research results show that the average identification rate can reach as high as 92.5%.