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Classification of Partial Discharge Signals by Combining Adaptive Local Iterative Filtering and Entropy Features
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 previousl...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5856049/ https://www.ncbi.nlm.nih.gov/pubmed/29385030 http://dx.doi.org/10.3390/s18020406 |
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author | Mitiche, Imene Morison, Gordon Nesbitt, Alan Hughes-Narborough, Michael Stewart, Brian G. Boreham, Philip |
author_facet | Mitiche, Imene Morison, Gordon Nesbitt, Alan Hughes-Narborough, Michael Stewart, Brian G. Boreham, Philip |
author_sort | Mitiche, Imene |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-5856049 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-58560492018-03-20 Classification of Partial Discharge Signals by Combining Adaptive Local Iterative Filtering and Entropy Features Mitiche, Imene Morison, Gordon Nesbitt, Alan Hughes-Narborough, Michael Stewart, Brian G. Boreham, Philip Sensors (Basel) Article 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. MDPI 2018-01-31 /pmc/articles/PMC5856049/ /pubmed/29385030 http://dx.doi.org/10.3390/s18020406 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Mitiche, Imene Morison, Gordon Nesbitt, Alan Hughes-Narborough, Michael Stewart, Brian G. Boreham, Philip Classification of Partial Discharge Signals by Combining Adaptive Local Iterative Filtering and Entropy Features |
title | Classification of Partial Discharge Signals by Combining Adaptive Local Iterative Filtering and Entropy Features |
title_full | Classification of Partial Discharge Signals by Combining Adaptive Local Iterative Filtering and Entropy Features |
title_fullStr | Classification of Partial Discharge Signals by Combining Adaptive Local Iterative Filtering and Entropy Features |
title_full_unstemmed | Classification of Partial Discharge Signals by Combining Adaptive Local Iterative Filtering and Entropy Features |
title_short | Classification of Partial Discharge Signals by Combining Adaptive Local Iterative Filtering and Entropy Features |
title_sort | classification of partial discharge signals by combining adaptive local iterative filtering and entropy features |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5856049/ https://www.ncbi.nlm.nih.gov/pubmed/29385030 http://dx.doi.org/10.3390/s18020406 |
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