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

I Mitiche, G Morison, M Hughes-Narborough, A Nesbitt, P Boreham, B G Stewart

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

Abstract

Electro-Magnetic Interference (EMI) is a measurement technique for Partial Discharge (PD) signals which arise in operating electrical machines, generators and other auxiliary equipment due to insulation degradation. Assessment of PD can help to reduce machine downtime and circumvent high replacement and maintenance costs. EMI signals can be complex to analyze due to their nonstationary nature. In this paper, a software condition-monitoring model is presented and a novel feature extraction technique, suitable for nonstationary EMI signals, is developed. This method maps multiple discharge sources signals, including PD, from the time domain to a feature space which aids interpretation of subsequent fault information. Results show excellent performance in classifying the different discharge sources.
LanguageEnglish
Title of host publication2017 IEEE Conference on Electrical Insulation and Dielectric Phenomena
Pages335-338
Number of pages4
Publication statusAccepted/In press - 10 Oct 2017

Fingerprint

Partial discharges
Signal interference
Entropy
Auxiliary equipment
Condition monitoring
Insulation
Feature extraction
Degradation
Costs

Keywords

  • electro-magnetic interference
  • partial discharge
  • discharge sources

Cite this

Mitiche, I., Morison, G., Hughes-Narborough, M., Nesbitt, A., Boreham, P., & Stewart, B. G. (Accepted/In press). Classification of partial discharge signals by combining adaptive local iterative filtering and entropy features. In 2017 IEEE Conference on Electrical Insulation and Dielectric Phenomena (pp. 335-338)
Mitiche, I ; Morison, G ; Hughes-Narborough, M ; Nesbitt, A ; Boreham, P ; Stewart, B G. / Classification of partial discharge signals by combining adaptive local iterative filtering and entropy features. 2017 IEEE Conference on Electrical Insulation and Dielectric Phenomena. 2017. pp. 335-338
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Mitiche, I, Morison, G, Hughes-Narborough, M, Nesbitt, A, Boreham, P & Stewart, BG 2017, Classification of partial discharge signals by combining adaptive local iterative filtering and entropy features. in 2017 IEEE Conference on Electrical Insulation and Dielectric Phenomena. pp. 335-338.

Classification of partial discharge signals by combining adaptive local iterative filtering and entropy features. / Mitiche, I; Morison, G; Hughes-Narborough, M; Nesbitt, A; Boreham, P; Stewart, B G.

2017 IEEE Conference on Electrical Insulation and Dielectric Phenomena. 2017. p. 335-338.

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

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Mitiche I, Morison G, Hughes-Narborough M, Nesbitt A, Boreham P, Stewart BG. Classification of partial discharge signals by combining adaptive local iterative filtering and entropy features. In 2017 IEEE Conference on Electrical Insulation and Dielectric Phenomena. 2017. p. 335-338