Classification of partial discharge signals by combining adaptive local iterative filtering and entropy features

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Mitiche, I and Morison, G and Hughes-Narborough, M and Nesbitt, A and Boreham, P and Stewart, B G; (2017) Classification of partial discharge signals by combining adaptive local iterative filtering and entropy features. In: 2017 IEEE Conference on Electrical Insulation and Dielectric Phenomena. IEEE Dielectrics and and Electrical Insulation Society, pp. 335-338. (In Press)

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Abstract

Electro-Magnetic Interference (EMI) is a measurement technique for Partial Discharge (PD) signals which arise in operating electrical machines, generators and other auxiliary equipment due to insulation degradation. Assessment of PD can help to reduce machine downtime and circumvent high replacement and maintenance costs. EMI signals can be complex to analyze due to their nonstationary nature. In this paper, a software condition-monitoring model is presented and a novel feature extraction technique, suitable for nonstationary EMI signals, is developed. This method maps multiple discharge sources signals, including PD, from the time domain to a feature space which aids interpretation of subsequent fault information. Results show excellent performance in classifying the different discharge sources.

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