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Entropy-Based Feature Extraction for Electromagnetic Discharges Classification in High-Voltage Power Generation

1
Department of Engineering, Glasgow Caledonian University, 70 Cowcaddens Rd, Glasgow G4 0BA, UK
2
Institute of Energy and Environment, University of Strathclyde, 204 George St, Glasgow G1 1XW, UK
3
Innovation Centre for Online Systems, 7 Townsend Business Park, Bere Regis BH20 7LA, UK
*
Author to whom correspondence should be addressed.
Entropy 2018, 20(8), 549; https://doi.org/10.3390/e20080549
Received: 22 May 2018 / Revised: 19 July 2018 / Accepted: 20 July 2018 / Published: 25 July 2018
(This article belongs to the Special Issue Entropy: From Physics to Information Sciences and Geometry)
This work exploits four entropy measures known as Sample, Permutation, Weighted Permutation, and Dispersion Entropy to extract relevant information from Electromagnetic Interference (EMI) discharge signals that are useful in fault diagnosis of High-Voltage (HV) equipment. Multi-class classification algorithms are used to classify or distinguish between various discharge sources such as Partial Discharges (PD), Exciter, Arcing, micro Sparking and Random Noise. The signals were measured and recorded on different sites followed by EMI expert’s data analysis in order to identify and label the discharge source type contained within the signal. The classification was performed both within each site and across all sites. The system performs well for both cases with extremely high classification accuracy within site. This work demonstrates the ability to extract relevant entropy-based features from EMI discharge sources from time-resolved signals requiring minimal computation making the system ideal for a potential application to online condition monitoring based on EMI. View Full-Text
Keywords: EMI measurement; partial discharge; entropy; classification; experts system; EMI discharge sources EMI measurement; partial discharge; entropy; classification; experts system; EMI discharge sources
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Mitiche, I.; Morison, G.; Nesbitt, A.; Stewart, B.G.; Boreham, P. Entropy-Based Feature Extraction for Electromagnetic Discharges Classification in High-Voltage Power Generation. Entropy 2018, 20, 549.

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