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Partial discharge diagnosis in GIS based on pulse sequence features and optimized machine learning classification techniques

Partial discharge (PD) diagnostics in Gas Insulated Switchgear (GIS) is important for reliable and secure operation of electrical utilities. Different techniques were used for PD diagnosis in GIS. In this work, PD diagnosis in GIS is proposed based on PD pulse sequence. PD pulse sequence only requir...

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Main Authors: Mansour, Diaa-Eldin A., Taha, Ibrahim B.m., Farade, Rizwan A., Abdul Wahab, Noor Izzri
Format: Article
Published: Elsevier 2022
Online Access:http://psasir.upm.edu.my/id/eprint/102663/
https://linkinghub.elsevier.com/retrieve/pii/S0378779622003777
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spelling my.upm.eprints.1026632024-06-29T14:17:00Z http://psasir.upm.edu.my/id/eprint/102663/ Partial discharge diagnosis in GIS based on pulse sequence features and optimized machine learning classification techniques Mansour, Diaa-Eldin A. Taha, Ibrahim B.m. Farade, Rizwan A. Abdul Wahab, Noor Izzri Partial discharge (PD) diagnostics in Gas Insulated Switchgear (GIS) is important for reliable and secure operation of electrical utilities. Different techniques were used for PD diagnosis in GIS. In this work, PD diagnosis in GIS is proposed based on PD pulse sequence. PD pulse sequence only requires the measurement of PD phase appearance and its corresponding instantaneous voltage. The PD diagnosis of various defect types is implemented using five optimized machine learning classification techniques: decision tree classification, ensemble methods, k-nearest neighbouring, Discriminant analysis, and Naïve Bayes classification. The features used for PD pulse sequence are the voltage change and phase angle change between successive PD pulses. Three scenarios are proposed for predicting the defect types in GIS. The first scenario is built based on the extracted features for two successive PD pulses, the second scenario is built based on the extracted features for three successive PD pulses, while the last scenario is built based on the extracted features for four successive PD pulses. The results illustrate the superior detecting accuracy of the second scenario with the proposed five ML classification techniques. The optimized ML classification techniques are implemented and carried out based on MATLAB software package. The ensemble classification method exhibited the highest accuracy for PD-based diagnosis in GIS with an overall accuracy of 97.1%. Elsevier 2022 Article PeerReviewed Mansour, Diaa-Eldin A. and Taha, Ibrahim B.m. and Farade, Rizwan A. and Abdul Wahab, Noor Izzri (2022) Partial discharge diagnosis in GIS based on pulse sequence features and optimized machine learning classification techniques. Electric Power Systems Research, 211. art. no. 108162. pp. 1-8. ISSN 0378-7796; ESSN: 1873-2046 https://linkinghub.elsevier.com/retrieve/pii/S0378779622003777 10.1016/j.epsr.2022.108162
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
description Partial discharge (PD) diagnostics in Gas Insulated Switchgear (GIS) is important for reliable and secure operation of electrical utilities. Different techniques were used for PD diagnosis in GIS. In this work, PD diagnosis in GIS is proposed based on PD pulse sequence. PD pulse sequence only requires the measurement of PD phase appearance and its corresponding instantaneous voltage. The PD diagnosis of various defect types is implemented using five optimized machine learning classification techniques: decision tree classification, ensemble methods, k-nearest neighbouring, Discriminant analysis, and Naïve Bayes classification. The features used for PD pulse sequence are the voltage change and phase angle change between successive PD pulses. Three scenarios are proposed for predicting the defect types in GIS. The first scenario is built based on the extracted features for two successive PD pulses, the second scenario is built based on the extracted features for three successive PD pulses, while the last scenario is built based on the extracted features for four successive PD pulses. The results illustrate the superior detecting accuracy of the second scenario with the proposed five ML classification techniques. The optimized ML classification techniques are implemented and carried out based on MATLAB software package. The ensemble classification method exhibited the highest accuracy for PD-based diagnosis in GIS with an overall accuracy of 97.1%.
format Article
author Mansour, Diaa-Eldin A.
Taha, Ibrahim B.m.
Farade, Rizwan A.
Abdul Wahab, Noor Izzri
spellingShingle Mansour, Diaa-Eldin A.
Taha, Ibrahim B.m.
Farade, Rizwan A.
Abdul Wahab, Noor Izzri
Partial discharge diagnosis in GIS based on pulse sequence features and optimized machine learning classification techniques
author_facet Mansour, Diaa-Eldin A.
Taha, Ibrahim B.m.
Farade, Rizwan A.
Abdul Wahab, Noor Izzri
author_sort Mansour, Diaa-Eldin A.
title Partial discharge diagnosis in GIS based on pulse sequence features and optimized machine learning classification techniques
title_short Partial discharge diagnosis in GIS based on pulse sequence features and optimized machine learning classification techniques
title_full Partial discharge diagnosis in GIS based on pulse sequence features and optimized machine learning classification techniques
title_fullStr Partial discharge diagnosis in GIS based on pulse sequence features and optimized machine learning classification techniques
title_full_unstemmed Partial discharge diagnosis in GIS based on pulse sequence features and optimized machine learning classification techniques
title_sort partial discharge diagnosis in gis based on pulse sequence features and optimized machine learning classification techniques
publisher Elsevier
publishDate 2022
url http://psasir.upm.edu.my/id/eprint/102663/
https://linkinghub.elsevier.com/retrieve/pii/S0378779622003777
_version_ 1803336776602877952
score 13.211869

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