Machine Learning Based Protection Scheme for Low Voltage AC Microgrids

Uzair, M; Eskandari, M; Li, L; Zhu, J

Machine Learning Based Protection Scheme for Low Voltage AC Microgrids

Uzair, M

Eskandari, M Li, L

Zhu, J

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Publisher:: MDPI
Publication Type:: Journal Article
Citation:: Energies, 2022, 15, (24)
Issue Date:: 2022-12-01

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Field	Value	Language
dc.contributor.author	Uzair, M https://orcid.org/0000-0003-2942-6163
dc.contributor.author	Eskandari, M
dc.contributor.author	Li, L https://orcid.org/0000-0001-8485-2216
dc.contributor.author	Zhu, J https://orcid.org/0000-0002-9763-4047
dc.date.accessioned	2023-04-12T23:49:22Z
dc.date.available	2023-04-12T23:49:22Z
dc.date.issued	2022-12-01
dc.identifier.citation	Energies, 2022, 15, (24)
dc.identifier.issn	1996-1073
dc.identifier.issn	1996-1073
dc.identifier.uri	http://hdl.handle.net/10453/169689
dc.description.abstract	The microgrid (MG) is a popular concept to handle the high penetration of distributed energy resources, such as renewable and energy storage systems, into electric grids. However, the integration of inverter-interfaced distributed generation units (IIDGs) imposes control and protection challenges. Fault identification, classification and isolation are major concerns with IIDGs-based active MGs where IIDGs reveal arbitrary impedance and thus different fault characteristics. Moreover, bidirectional complex power flow creates extra difficulties for fault analysis. This makes the conventional methods inefficient, and a new paradigm in protection schemes is needed for IIDGs-dominated MGs. In this paper, a machine-learning (ML)-based protection technique is developed for IIDG-based AC MGs by extracting unique and novel features for detecting and classifying symmetrical and unsymmetrical faults. Different signals, namely, 400 samples, for wide variations in operating conditions of an MG are obtained through electromagnetic transient simulations in DIgSILENT PowerFactory. After retrieving and pre-processing the signals, 10 different feature extraction techniques, including new peaks metric and max factor, are applied to obtain 100 features. They are ranked using the Kruskal–Wallis H-Test to identify the best performing features, apart from estimating predictor importance for ensemble ML classification. The top 18 features are used as input to train 35 classification learners. Random Forest (RF) outperformed all other ML classifiers for fault detection and fault type classification with faulted phase identification. Compared to previous methods, the results show better performance of the proposed method.
dc.language	English
dc.publisher	MDPI
dc.relation.ispartof	Energies
dc.relation.isbasedon	10.3390/en15249397
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	02 Physical Sciences, 09 Engineering
dc.title	Machine Learning Based Protection Scheme for Low Voltage AC Microgrids
dc.type	Journal Article
utslib.citation.volume	15
utslib.for	02 Physical Sciences
utslib.for	09 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
utslib.copyright.status	open_access	*
dc.date.updated	2023-04-12T23:49:21Z
pubs.issue	24
pubs.publication-status	Published
pubs.volume	15
utslib.citation.issue	24

Abstract:

The microgrid (MG) is a popular concept to handle the high penetration of distributed energy resources, such as renewable and energy storage systems, into electric grids. However, the integration of inverter-interfaced distributed generation units (IIDGs) imposes control and protection challenges. Fault identification, classification and isolation are major concerns with IIDGs-based active MGs where IIDGs reveal arbitrary impedance and thus different fault characteristics. Moreover, bidirectional complex power flow creates extra difficulties for fault analysis. This makes the conventional methods inefficient, and a new paradigm in protection schemes is needed for IIDGs-dominated MGs. In this paper, a machine-learning (ML)-based protection technique is developed for IIDG-based AC MGs by extracting unique and novel features for detecting and classifying symmetrical and unsymmetrical faults. Different signals, namely, 400 samples, for wide variations in operating conditions of an MG are obtained through electromagnetic transient simulations in DIgSILENT PowerFactory. After retrieving and pre-processing the signals, 10 different feature extraction techniques, including new peaks metric and max factor, are applied to obtain 100 features. They are ranked using the Kruskal–Wallis H-Test to identify the best performing features, apart from estimating predictor importance for ensemble ML classification. The top 18 features are used as input to train 35 classification learners. Random Forest (RF) outperformed all other ML classifiers for fault detection and fault type classification with faulted phase identification. Compared to previous methods, the results show better performance of the proposed method.

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