Condition Monitoring and Maintenance Management with Grid-Connected Renewable Energy Systems

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dc.contributor.authorAlam, Md. Mottahircs
dc.contributor.authorHaque, Ahteshamulcs
dc.contributor.authorKhan, Mohammed Alics
dc.contributor.authorSobahi, Nebras M.cs
dc.contributor.authorMehedi, Ibrahim Mustafacs
dc.contributor.authorKhan, Asif Irshadcs
dc.coverage.issue2cs
dc.coverage.volume72cs
dc.date.accessioned2023-01-17T15:53:14Z
dc.date.available2023-01-17T15:53:14Z
dc.date.issued2022-03-29cs
dc.description.abstractThe shift towards the renewable energy market for carbon-neutral power generation has encouraged different governments to come up with a plan of action. But with the endorsement of renewable energy for harsh environmental conditions like sand dust and snow, monitoring and maintenance are a few of the prime concerns. These problems were addressed widely in the literature, but most of the research has drawbacks due to long detection time, and high misclassification error. Hence to overcome these drawbacks, and to develop an accurate monitoring approach, this paper is motivated toward the understanding of primary failure concerning a grid-connected photovoltaic (PV) system and highlighted along with a brief overview on existing fault detection methodology. Based on the drawback a data-driven machine learning approach has been used for the identification of fault and indicating the maintenance unit regarding the operation and maintenance requirement. Further, the system was tested with a 4 kWp grid-connected PV system, and a decision tree-based algorithm was developed for the identification of a fault. The results identified 94.7% training accuracy and 14000 observations/sec prediction speed for the trained classifier and improved the reliability of fault detection nature of the grid-connected PV operation.en
dc.formattextcs
dc.format.extent3999-4017cs
dc.format.mimetypeapplication/pdfcs
dc.identifier.citationCMC-Computers Materials & Continua. 2022, vol. 72, issue 2, p. 3999-4017.en
dc.identifier.doi10.32604/cmc.2022.026353cs
dc.identifier.issn1546-2218cs
dc.identifier.other177645cs
dc.identifier.urihttp://hdl.handle.net/11012/204172
dc.language.isoencs
dc.publisherTech Science Presscs
dc.relation.ispartofCMC-Computers Materials & Continuacs
dc.relation.urihttps://www.techscience.com/cmc/v72n2/47234cs
dc.rightsCreative Commons Attribution 4.0 Internationalcs
dc.rights.accessopenAccesscs
dc.rights.sherpahttp://www.sherpa.ac.uk/romeo/issn/1546-2218/cs
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/cs
dc.subjectFault detectionen
dc.subjectmachine learningen
dc.subjectfault ride throughen
dc.titleCondition Monitoring and Maintenance Management with Grid-Connected Renewable Energy Systemsen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
sync.item.dbidVAV-177645en
sync.item.dbtypeVAVen
sync.item.insts2023.01.17 16:53:14en
sync.item.modts2023.01.17 16:14:27en
thesis.grantorVysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií. Ústav elektroenergetikycs
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