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

Imene Mitiche, Gordon Morison*, Alan Nesbitt, Michael Hughes-Narborough, Brian G. Stewart, Philip Boreham

*Corresponding author for this work

Research output: Contribution to journalArticle

91 Downloads (Pure)

Abstract

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.
Original languageEnglish
Article number406
Number of pages14
JournalSensors
Volume18
Issue number2
DOIs
Publication statusPublished - 31 Jan 2018

Keywords

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

Fingerprint Dive into the research topics of 'Classification of partial discharge signals by combining adaptive local iterative filtering and entropy features'. Together they form a unique fingerprint.

  • Cite this