Statistical Feature Extraction Combined with Generalized Discriminant Component Analysis Driven SVM for Fault Diagnosis of HVDC GIS

Accurately identifying the types of insulation defects inside a gas-insulated switchgear (GIS) is of great significance for guiding maintenance work as well as ensuring the safe and stable operation of GIS. By building a set of 220 kV high-voltage direct current (HVDC) GIS experiment platforms and m...

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Autores principales:	Ruixu Zhou, Wensheng Gao, Weidong Liu, Dengwei Ding, Bowen Zhang
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	HVDC GIS fault diagnosis pulse current measurement statistical feature extraction generalized discriminant component analysis SVM Technology T
Acceso en línea:	https://doaj.org/article/0068b1f7c1464b629ef42e3cc15828e3
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id	oai:doaj.org-article:0068b1f7c1464b629ef42e3cc15828e3
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spelling	oai:doaj.org-article:0068b1f7c1464b629ef42e3cc15828e32021-11-25T17:27:49ZStatistical Feature Extraction Combined with Generalized Discriminant Component Analysis Driven SVM for Fault Diagnosis of HVDC GIS10.3390/en142276741996-1073https://doaj.org/article/0068b1f7c1464b629ef42e3cc15828e32021-11-01T00:00:00Zhttps://www.mdpi.com/1996-1073/14/22/7674https://doaj.org/toc/1996-1073Accurately identifying the types of insulation defects inside a gas-insulated switchgear (GIS) is of great significance for guiding maintenance work as well as ensuring the safe and stable operation of GIS. By building a set of 220 kV high-voltage direct current (HVDC) GIS experiment platforms and manufacturing four different types of insulation defects (including multiple sizes and positions), 180,828 pulse current signals under multiple voltage levels are successfully measured. Then, the apparent discharge quantity and the discharge time, two inherent physical quantities unaffected by the experimental platform and measurement system, are obtained after the pulse current signal is denoised, according to which 70 statistical features are extracted. In this paper, a pattern recognition method based on generalized discriminant component analysis driven support vector machine (SVM) is detailed and the corresponding selection criterion of involved parameters is established. The results show that the newly proposed pattern recognition method greatly improves the recognition accuracy of fault diagnosis in comparison with 36 kinds of state-of-the-art dimensionality reduction algorithms and 44 kinds of state-of-the-art classifiers. This newly proposed method not only solves the difficulty that phase-resolved partial discharge (PRPD) cannot be applied under DC condition but also immensely facilitates the fault diagnosis of HVDC GIS.Ruixu ZhouWensheng GaoWeidong LiuDengwei DingBowen ZhangMDPI AGarticleHVDC GISfault diagnosispulse current measurementstatistical feature extractiongeneralized discriminant component analysisSVMTechnologyTENEnergies, Vol 14, Iss 7674, p 7674 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	HVDC GIS fault diagnosis pulse current measurement statistical feature extraction generalized discriminant component analysis SVM Technology T
spellingShingle	HVDC GIS fault diagnosis pulse current measurement statistical feature extraction generalized discriminant component analysis SVM Technology T Ruixu Zhou Wensheng Gao Weidong Liu Dengwei Ding Bowen Zhang Statistical Feature Extraction Combined with Generalized Discriminant Component Analysis Driven SVM for Fault Diagnosis of HVDC GIS
description	Accurately identifying the types of insulation defects inside a gas-insulated switchgear (GIS) is of great significance for guiding maintenance work as well as ensuring the safe and stable operation of GIS. By building a set of 220 kV high-voltage direct current (HVDC) GIS experiment platforms and manufacturing four different types of insulation defects (including multiple sizes and positions), 180,828 pulse current signals under multiple voltage levels are successfully measured. Then, the apparent discharge quantity and the discharge time, two inherent physical quantities unaffected by the experimental platform and measurement system, are obtained after the pulse current signal is denoised, according to which 70 statistical features are extracted. In this paper, a pattern recognition method based on generalized discriminant component analysis driven support vector machine (SVM) is detailed and the corresponding selection criterion of involved parameters is established. The results show that the newly proposed pattern recognition method greatly improves the recognition accuracy of fault diagnosis in comparison with 36 kinds of state-of-the-art dimensionality reduction algorithms and 44 kinds of state-of-the-art classifiers. This newly proposed method not only solves the difficulty that phase-resolved partial discharge (PRPD) cannot be applied under DC condition but also immensely facilitates the fault diagnosis of HVDC GIS.
format	article
author	Ruixu Zhou Wensheng Gao Weidong Liu Dengwei Ding Bowen Zhang
author_facet	Ruixu Zhou Wensheng Gao Weidong Liu Dengwei Ding Bowen Zhang
author_sort	Ruixu Zhou
title	Statistical Feature Extraction Combined with Generalized Discriminant Component Analysis Driven SVM for Fault Diagnosis of HVDC GIS
title_short	Statistical Feature Extraction Combined with Generalized Discriminant Component Analysis Driven SVM for Fault Diagnosis of HVDC GIS
title_full	Statistical Feature Extraction Combined with Generalized Discriminant Component Analysis Driven SVM for Fault Diagnosis of HVDC GIS
title_fullStr	Statistical Feature Extraction Combined with Generalized Discriminant Component Analysis Driven SVM for Fault Diagnosis of HVDC GIS
title_full_unstemmed	Statistical Feature Extraction Combined with Generalized Discriminant Component Analysis Driven SVM for Fault Diagnosis of HVDC GIS
title_sort	statistical feature extraction combined with generalized discriminant component analysis driven svm for fault diagnosis of hvdc gis
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/0068b1f7c1464b629ef42e3cc15828e3
work_keys_str_mv	AT ruixuzhou statisticalfeatureextractioncombinedwithgeneralizeddiscriminantcomponentanalysisdrivensvmforfaultdiagnosisofhvdcgis AT wenshenggao statisticalfeatureextractioncombinedwithgeneralizeddiscriminantcomponentanalysisdrivensvmforfaultdiagnosisofhvdcgis AT weidongliu statisticalfeatureextractioncombinedwithgeneralizeddiscriminantcomponentanalysisdrivensvmforfaultdiagnosisofhvdcgis AT dengweiding statisticalfeatureextractioncombinedwithgeneralizeddiscriminantcomponentanalysisdrivensvmforfaultdiagnosisofhvdcgis AT bowenzhang statisticalfeatureextractioncombinedwithgeneralizeddiscriminantcomponentanalysisdrivensvmforfaultdiagnosisofhvdcgis
_version_	1718412326144573440

Statistical Feature Extraction Combined with Generalized Discriminant Component Analysis Driven SVM for Fault Diagnosis of HVDC GIS

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