Machine learning scopes on microgrid predictive maintenance: Potential frameworks, challenges, and prospects

Arafat, MY; Hossain, MJ; Alam, MM

Machine learning scopes on microgrid predictive maintenance: Potential frameworks, challenges, and prospects

Arafat, MY Hossain, MJ Alam, MM

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Publisher:: PERGAMON-ELSEVIER SCIENCE LTD
Publication Type:: Journal Article
Citation:: Renewable and Sustainable Energy Reviews, 2024, 190
Issue Date:: 2024-02-01

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Full metadata record

Field	Value	Language
dc.contributor.author	Arafat, MY
dc.contributor.author	Hossain, MJ
dc.contributor.author	Alam, MM
dc.date.accessioned	2024-05-03T01:06:22Z
dc.date.available	2024-05-03T01:06:22Z
dc.date.issued	2024-02-01
dc.identifier.citation	Renewable and Sustainable Energy Reviews, 2024, 190
dc.identifier.issn	1364-0321
dc.identifier.issn	1879-0690
dc.identifier.uri	http://hdl.handle.net/10453/178566
dc.description.abstract	Predictive maintenance is an essential aspect of microgrid operations as it enables identifying potential equipment failures in advance, reducing downtime, and increasing the overall efficiency of the system. Machine learning-based techniques have a great potential to be effective in improving the accuracy of failure predictions, detecting, and diagnosing faults in real-time, and monitoring the health and remaining useful life of microgrid components. The integration of these techniques with microgrid components can lead to reduced downtime, improved safety, overall efficiency, and sustainability. This work aims to explore the research scope of machine learning-based predictive maintenance in microgrid systems. The analysis provides a comprehensive review of the state-of-the-art machine learning techniques that could be used for microgrid predictive maintenance and highlights the gaps and challenges that need to be addressed. This study suggests future research directions in the field and frameworks to improve predictive maintenance using machine learning for microgrid industries.
dc.language	English
dc.publisher	PERGAMON-ELSEVIER SCIENCE LTD
dc.relation.ispartof	Renewable and Sustainable Energy Reviews
dc.relation.isbasedon	10.1016/j.rser.2023.114088
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject	09 Engineering
dc.subject.classification	Energy
dc.subject.classification	40 Engineering
dc.title	Machine learning scopes on microgrid predictive maintenance: Potential frameworks, challenges, and prospects
dc.type	Journal Article
utslib.citation.volume	190
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	embargoed	*
utslib.copyright.embargo	2026-02-01T00:00:00+1000Z
dc.date.updated	2024-05-03T01:06:19Z
pubs.publication-status	Published
pubs.volume	190

Abstract:

Predictive maintenance is an essential aspect of microgrid operations as it enables identifying potential equipment failures in advance, reducing downtime, and increasing the overall efficiency of the system. Machine learning-based techniques have a great potential to be effective in improving the accuracy of failure predictions, detecting, and diagnosing faults in real-time, and monitoring the health and remaining useful life of microgrid components. The integration of these techniques with microgrid components can lead to reduced downtime, improved safety, overall efficiency, and sustainability. This work aims to explore the research scope of machine learning-based predictive maintenance in microgrid systems. The analysis provides a comprehensive review of the state-of-the-art machine learning techniques that could be used for microgrid predictive maintenance and highlights the gaps and challenges that need to be addressed. This study suggests future research directions in the field and frameworks to improve predictive maintenance using machine learning for microgrid industries.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/178566