Application of grey clustering approach and genetic algorithm to partial discharge pattern recognition

Authors:
Wen-Yeau Chang
Affiliations:
Department of Electrical Engineering, St. John's University, Tamsui, Taipei, Taiwan, Taiwan
Venue:
WSEAS TRANSACTIONS on SYSTEMS
Year:
2009

Citing 3
Cited 0

Partial discharge classification using neural networks and statistical parameters

IMCAS'07 Proceedings of the 6th WSEAS International Conference on Instrumentation, Measurement, Circuits and Systems
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Partial discharge pattern recognition of cast-resin current transformers using fuzzy C-means clustering approach

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Abstract

Partial discharge (PD) measurement and recognition is a significant tool for potential failure diagnosis of the high-voltage equipment. This paper proposes the application of grey clustering approach (GCA) to recognize partial discharge patterns of the high-voltage equipment. The PD patterns are measured by using a commercial PD detector. A set of features, used as operators, for each PD pattern is extracted through statistical schemes. The significant features of PD patterns are extracted by using the genetic algorithm (GA). The proposed grey clustering approach has the advantages of high robustness and effectiveness to ambiguous patterns and is useful in recognizing the PD patterns of the high-voltage equipment. To verify the effectiveness of the proposed method, the grey clustering approach was verified on two types of high-voltage equipments. The test results show that the proposed approach may achieve quite satisfactory recognition of PD patterns.