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 journalArticlepeer-review

6 Citations (Scopus)
30 Downloads (Pure)


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.
Original languageEnglish
Article number549
Number of pages20
Issue number8
Publication statusPublished - 25 Jul 2018


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


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