Classification of multiple electromagnetic interference events in high-voltage power plant

Imene Mitiche; Gordon Morison; Alan Nesbitt; Michael Hughes-Narborough; Brian G. Stewart; Philip  Boreham

doi:10.1109/UPEC.2018.8541931

Classification of multiple electromagnetic interference events in high-voltage power plant

Imene Mitiche, Gordon Morison, Alan Nesbitt, Michael Hughes-Narborough, Brian G. Stewart, Philip Boreham

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

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Abstract

This paper addresses condition assessment of electrical assets contained in high voltage power plants. Our work introduces a novel analysis approach of multiple event signals related to faults, and which are measured using Electro-Magnetic Interference method. The proposed method transfers the experts’ knowledge on events presence in the signals to an intelligent system which could potentially be used for automatic EMI diagnosis. Cyclic spectrum analysis is used as feature extraction to efficiently extract the repetitive rate and the dynamic discharge level of the events, and multi-class support vector machine is adopted for their classification. This first and novel method achieved successful results which may have potential implications on developing a framework for automatic diagnosis tool of EMI events.

Original language	English
Title of host publication	2018 53rd International Universities Power Engineering Conference (UPEC)
Publisher	IEEE
Number of pages	4
ISBN (Print)	9781538629109
DOIs	https://doi.org/10.1109/UPEC.2018.8541931
Publication status	Published - 13 Dec 2018

Keywords

high voltage
electromagnetic interference

Documents and Links

10.1109/UPEC.2018.8541931

Mitiche, I. et al (2018) Classification of multiple electromagnetic interference events in high voltage power plant
Copyright © 2018 IEEE. This is a post-peer review, pre-copy edited version of the article and may differ slightly from the final published version of the work. This work is made available in line with the publisher policy. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
Accepted author manuscript, 1.29 MB

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Mitiche, I., Morison, G., Nesbitt, A., Hughes-Narborough, M., Stewart, B. G., & Boreham, P. (2018). Classification of multiple electromagnetic interference events in high-voltage power plant. In 2018 53rd International Universities Power Engineering Conference (UPEC) IEEE. https://doi.org/10.1109/UPEC.2018.8541931

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title = "Classification of multiple electromagnetic interference events in high-voltage power plant",

abstract = "This paper addresses condition assessment of electrical assets contained in high voltage power plants. Our work introduces a novel analysis approach of multiple event signals related to faults, and which are measured using Electro-Magnetic Interference method. The proposed method transfers the experts{\textquoteright} knowledge on events presence in the signals to an intelligent system which could potentially be used for automatic EMI diagnosis. Cyclic spectrum analysis is used as feature extraction to efficiently extract the repetitive rate and the dynamic discharge level of the events, and multi-class support vector machine is adopted for their classification. This first and novel method achieved successful results which may have potential implications on developing a framework for automatic diagnosis tool of EMI events.",

keywords = "high voltage, electromagnetic interference",

author = "Imene Mitiche and Gordon Morison and Alan Nesbitt and Michael Hughes-Narborough and Stewart, {Brian G.} and Philip Boreham",

note = "Acceptance in SAN AAM: no embargo No ISSN found for output, out of policy scope. ST 13/11/19",

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month = dec,

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Classification of multiple electromagnetic interference events in high-voltage power plant. / Mitiche, Imene ; Morison, Gordon ; Nesbitt, Alan; Hughes-Narborough, Michael; Stewart, Brian G.; Boreham, Philip .

2018 53rd International Universities Power Engineering Conference (UPEC). IEEE, 2018.

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

TY - GEN

T1 - Classification of multiple electromagnetic interference events in high-voltage power plant

AU - Mitiche, Imene

AU - Morison, Gordon

AU - Nesbitt, Alan

AU - Hughes-Narborough, Michael

AU - Stewart, Brian G.

AU - Boreham, Philip

N1 - Acceptance in SAN AAM: no embargo No ISSN found for output, out of policy scope. ST 13/11/19

PY - 2018/12/13

Y1 - 2018/12/13

N2 - This paper addresses condition assessment of electrical assets contained in high voltage power plants. Our work introduces a novel analysis approach of multiple event signals related to faults, and which are measured using Electro-Magnetic Interference method. The proposed method transfers the experts’ knowledge on events presence in the signals to an intelligent system which could potentially be used for automatic EMI diagnosis. Cyclic spectrum analysis is used as feature extraction to efficiently extract the repetitive rate and the dynamic discharge level of the events, and multi-class support vector machine is adopted for their classification. This first and novel method achieved successful results which may have potential implications on developing a framework for automatic diagnosis tool of EMI events.

AB - This paper addresses condition assessment of electrical assets contained in high voltage power plants. Our work introduces a novel analysis approach of multiple event signals related to faults, and which are measured using Electro-Magnetic Interference method. The proposed method transfers the experts’ knowledge on events presence in the signals to an intelligent system which could potentially be used for automatic EMI diagnosis. Cyclic spectrum analysis is used as feature extraction to efficiently extract the repetitive rate and the dynamic discharge level of the events, and multi-class support vector machine is adopted for their classification. This first and novel method achieved successful results which may have potential implications on developing a framework for automatic diagnosis tool of EMI events.

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KW - electromagnetic interference

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