Classification of Partial Discharge Signals by Combining Adaptive Local Iterative Filtering and Entropy Features

Sensors

Alan Nesbitt¹  Imene Mitiche¹  Gordon Morison¹  Michael Hughes-Narborough¹  Philip Boreham²  Brian G. Stewart³ 

[1] Department of Engineering, Glasgow Caledonian University, 70 Cowcaddens Road, Glasgow G4 0BA, UK;Innovation Centre for Online Systems, 7 Townsend Business Park, Bere Regis BH20 7LA, UK;Institute of Energy and Environment, University of Strathclyde, 204 George Street, Glasgow G1 1XW, UK;

关键词: EMI method;    partial discharge;    permutation entropy;    dispersion entropy;    classification;    expert’s system;    EMI events (discharge sources);   

DOI  :  10.3390/s18020406

来源: DOAJ

【 摘 要 】

Electromagnetic Interference (EMI) is a technique for capturing Partial Discharge (PD) signals in High-Voltage (HV) power plant apparatus. EMI signals can be non-stationary which makes their analysis difficult, particularly for pattern recognition applications. This paper elaborates 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 the 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 expert’s 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.

【 授权许可】

Unknown