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

Mitiche, Imene and Morison, Gordon and Nesbitt, Alan and Hughes-Narborough, Michael and Stewart, Brian G. and Boreham, Philip (2018) Classification of partial discharge signals by combining adaptive local iterative filtering and entropy features. Sensors, 18 (2). 406. ISSN 1424-8220

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    Abstract

    Electromagnetic Interference (EMI) is a technique for capturing Partial Discharge (PD) signals in high voltage power plant apparatus. EMI signals can be non-stationary which makes their analysis difficult, particularly for pattern recognition applications. This paper, extends upon a previously developed software condition-monitoring model for improved EMI events classification based on time-frequency signal decomposition and entropy features. The idea of the proposed method is to map multiple discharge source signals captured by EMI and labelled by experts, including PD, from the time domain to a feature space which aids in interpretation of subsequent fault information. Here, instead of using only one permutation entropy measure a more robust measure, called Dispersion Entropy (DE), is added to the feature vector. Multi-Class Support Vector Machine (MCSVM) methods are utilized for classification of the different discharge sources. Results show an improved classification accuracy compared to previously proposed methods. This yields to a successful development of an experts knowledge-based intelligent system. Since this method is demonstrated to be successful with real field data, it brings the benefit of possible real world application for EMI condition monitoring.