Machine Learning Schemes for Anomaly Detection in Solar Power Plants

Ibrahim, M; Alsheikh, A; Awaysheh, FM; Alshehri, MD

Machine Learning Schemes for Anomaly Detection in Solar Power Plants

Ibrahim, M Alsheikh, A Awaysheh, FM Alshehri, MD

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Publisher:: MDPI
Publication Type:: Journal Article
Citation:: Energies, 2022, 15, (3)
Issue Date:: 2022-02-01

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Field	Value	Language
dc.contributor.author	Ibrahim, M
dc.contributor.author	Alsheikh, A
dc.contributor.author	Awaysheh, FM
dc.contributor.author	Alshehri, MD
dc.date.accessioned	2023-04-04T04:33:35Z
dc.date.available	2023-04-04T04:33:35Z
dc.date.issued	2022-02-01
dc.identifier.citation	Energies, 2022, 15, (3)
dc.identifier.issn	1996-1073
dc.identifier.issn	1996-1073
dc.identifier.uri	http://hdl.handle.net/10453/169106
dc.description.abstract	The rapid industrial growth in solar energy is gaining increasing interest in renewable power from smart grids and plants. Anomaly detection in photovoltaic (PV) systems is a demanding task. In this sense, it is vital to utilize the latest updates in machine learning technology to accurately and timely disclose different system anomalies. This paper addresses this issue by evaluating the performance of different machine learning schemes and applying them to detect anomalies on photovoltaic components. The following schemes are evaluated: AutoEncoder Long Short-Term Memory (AE-LSTM), Facebook-Prophet, and Isolation Forest. These models can identify the PV system’s healthy and abnormal actual behaviors. Our results provide clear insights to make an informed decision, especially with experimental trade-offs for such a complex solution space.
dc.language	English
dc.publisher	MDPI
dc.relation.ispartof	Energies
dc.relation.isbasedon	10.3390/en15031082
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	02 Physical Sciences, 09 Engineering
dc.title	Machine Learning Schemes for Anomaly Detection in Solar Power Plants
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 Computer Science
utslib.copyright.status	open_access	*
dc.date.updated	2023-04-04T04:33:33Z
pubs.issue	3
pubs.publication-status	Published
pubs.volume	15
utslib.citation.issue	3

Abstract:

The rapid industrial growth in solar energy is gaining increasing interest in renewable power from smart grids and plants. Anomaly detection in photovoltaic (PV) systems is a demanding task. In this sense, it is vital to utilize the latest updates in machine learning technology to accurately and timely disclose different system anomalies. This paper addresses this issue by evaluating the performance of different machine learning schemes and applying them to detect anomalies on photovoltaic components. The following schemes are evaluated: AutoEncoder Long Short-Term Memory (AE-LSTM), Facebook-Prophet, and Isolation Forest. These models can identify the PV system’s healthy and abnormal actual behaviors. Our results provide clear insights to make an informed decision, especially with experimental trade-offs for such a complex solution space.

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http://hdl.handle.net/10453/169106