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

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

Electronic And Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book

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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.

Original language	English
Title of host publication	2017 IEEE Conference on Electrical Insulation and Dielectric Phenomena
Pages	335-338
Number of pages	4
Publication status	Accepted/In press - 10 Oct 2017

Keywords

electro-magnetic interference
partial discharge
discharge sources

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Mitiche-etal-IEEE-CEIDP-2017-Classification-of-partial-discharge-signals-by-combiningAccepted author manuscript, 324 KB

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title = "Classification of partial discharge signals by combining adaptive local iterative filtering and entropy features",

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.",

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T1 - Classification of partial discharge signals by combining adaptive local iterative filtering and entropy features

AU - Mitiche, I

AU - Morison, G

AU - Hughes-Narborough, M

AU - Nesbitt, A

AU - Boreham, P

AU - Stewart, B G

PY - 2017/10/10

Y1 - 2017/10/10

N2 - 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.

AB - 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.

KW - electro-magnetic interference

KW - partial discharge

KW - discharge sources

UR - https://www.ieee.org/conferences_events/conferences/conferencedetails/index.html

M3 - Conference contribution book

SP - 335

EP - 338

BT - 2017 IEEE Conference on Electrical Insulation and Dielectric Phenomena

ER -

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

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