Entropy-based feature extraction for electromagnetic discharges classification in high-voltage power generation

Imene Mitiche, Gordon Morison, Alan Nesbitt, Brian G. Stewart, Philip Boreham

Research output: Contribution to journalArticle

Abstract

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.
LanguageEnglish
Article number549
Number of pages20
JournalEntropy
Volume20
Issue number8
DOIs
Publication statusPublished - 25 Jul 2018

Fingerprint

Signal interference
pattern recognition
electromagnetic interference
Power generation
Feature extraction
high voltages
Entropy
entropy
electromagnetism
Electric potential
permutations
Partial discharges
Condition monitoring
Electric sparks
time signals
Failure analysis
Labels
random noise

Keywords

  • EMI measurement
  • partial discharge
  • entropy
  • classification
  • expert system
  • EMI discharge sources

Cite this

Mitiche, Imene ; Morison, Gordon ; Nesbitt, Alan ; Stewart, Brian G. ; Boreham, Philip. / Entropy-based feature extraction for electromagnetic discharges classification in high-voltage power generation. In: Entropy. 2018 ; Vol. 20, No. 8.
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Entropy-based feature extraction for electromagnetic discharges classification in high-voltage power generation. / Mitiche, Imene; Morison, Gordon; Nesbitt, Alan; Stewart, Brian G.; Boreham, Philip.

In: Entropy, Vol. 20, No. 8, 549, 25.07.2018.

Research output: Contribution to journalArticle

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T1 - Entropy-based feature extraction for electromagnetic discharges classification in high-voltage power generation

AU - Mitiche, Imene

AU - Morison, Gordon

AU - Nesbitt, Alan

AU - Stewart, Brian G.

AU - Boreham, Philip

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AB - 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.

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KW - classification

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