Identifying line-to-ground faulted phase in low and medium voltage AC microgrid using principal component analysis and supervised machine-learning

Uzair, M; Li, L; Zhu, JG

Identifying line-to-ground faulted phase in low and medium voltage AC microgrid using principal component analysis and supervised machine-learning

Uzair, M

Li, L

Zhu, JG

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: Australasian Universities Power Engineering Conference, AUPEC 2018, 2019
Issue Date:: 2019-07-11

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Field	Value	Language
dc.contributor.author	Uzair, M https://orcid.org/0000-0003-2942-6163
dc.contributor.author	Li, L https://orcid.org/0000-0001-8485-2216
dc.contributor.author	Zhu, JG
dc.date	2018-11-27
dc.date.accessioned	2020-04-26T05:21:47Z
dc.date.available	2021-01-02T18:04:22Z
dc.date.issued	2019-07-11
dc.identifier.citation	Australasian Universities Power Engineering Conference, AUPEC 2018, 2019
dc.identifier.isbn	9781538684740
dc.identifier.uri	http://hdl.handle.net/10453/140282
dc.description.abstract	© 2018 IEEE. A supervised machine-learning based approach for faulted phase identification in bolted, low- A nd high-impedance line-to-ground faults using principal component analysis for feature extraction from multiple input signals is presented in this paper. DIgSILENT PowerFactory is used for simulating the underlying microgrid to obtain fault related data, while MATLAB is used for machine learning application. A 15-fold cross validation is applied to the training dataset for evaluation of different machine learning models and the results show supreme performance compared to previous methods.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	Australasian Universities Power Engineering Conference, AUPEC 2018
dc.relation.ispartof	Australasian Universities Power Engineering Conference
dc.relation.isbasedon	10.1109/AUPEC.2018.8757918
dc.rights	info:eu-repo/semantics/openAccess
dc.rights	© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, 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 component of this work in other works.	en_US
dc.title	Identifying line-to-ground faulted phase in low and medium voltage AC microgrid using principal component analysis and supervised machine-learning
dc.type	Conference Proceeding
utslib.location.activity	Auckland
utslib.for	0906 Electrical and Electronic Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CCET - Centre for Clean Energy Technology
utslib.copyright.status	open_access	*
pubs.consider-herdc	true
dc.date.updated	2020-04-26T05:21:44Z
pubs.finish-date	2018-11-30
pubs.publication-status	Published
pubs.start-date	2018-11-27

Abstract:

© 2018 IEEE. A supervised machine-learning based approach for faulted phase identification in bolted, low- A nd high-impedance line-to-ground faults using principal component analysis for feature extraction from multiple input signals is presented in this paper. DIgSILENT PowerFactory is used for simulating the underlying microgrid to obtain fault related data, while MATLAB is used for machine learning application. A 15-fold cross validation is applied to the training dataset for evaluation of different machine learning models and the results show supreme performance compared to previous methods.

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http://hdl.handle.net/10453/140282