Publication:
Deep learning methods for solar fault detection and classification: A review

Simple item page
dc.citedby9
dc.contributor.authorAl-Mashhadani R.en_US
dc.contributor.authorAlkawsi G.en_US
dc.contributor.authorBaashar Y.en_US
dc.contributor.authorAlkahtani A.A.en_US
dc.contributor.authorNordin F.H.en_US
dc.contributor.authorHashim W.en_US
dc.contributor.authorKiong T.S.en_US
dc.contributor.authorid57223341022en_US
dc.contributor.authorid57191982354en_US
dc.contributor.authorid56768090200en_US
dc.contributor.authorid55646765500en_US
dc.contributor.authorid25930510500en_US
dc.contributor.authorid11440260100en_US
dc.contributor.authorid57216824752en_US
dc.date.accessioned2023-05-29T09:07:57Z
dc.date.available2023-05-29T09:07:57Z
dc.date.issued2021
dc.description.abstractIn light of the continuous and rapid increase in reliance on solar energy as a suitable alternative to the conventional energy produced by fuel, maintenance becomes an inevitable matter for both producers and consumers alike. Electroluminescence technology is a useful technique in detecting solar panels� faults and determining their life span using artificial intelligence tools such as neural networks and others. In recent years, deep learning technology has emerged to open new horizons in the accuracy of learning and extract meaningful information from many applications, particularly those that depend mainly on images, such as the technique of electroluminescence. From the literature, it is noted that this part of the research has not received enough attention despite the importance that researchers have attached to it in the past few years. This paper reviews the most important research papers that rely on deep learning in studying solar energy failures in recent years.We compare deep and hybrid learning models and highlight the essential pros and cons of each research separately so that we provide the reader with a critical overview that may contribute positively to the development of research in this crucial field. � 2021 NSP Natural Sciences Publishing Cor.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.18576/isl/100213
dc.identifier.epage331
dc.identifier.issue2
dc.identifier.scopus2-s2.0-85105669437
dc.identifier.spage323
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85105669437&doi=10.18576%2fisl%2f100213&partnerID=40&md5=1a6823535442431e0e78f8f27809e6ee
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26222
dc.identifier.volume10
dc.publisherNatural Sciences Publishingen_US
dc.sourceScopus
dc.sourcetitleInformation Sciences Letters
dc.titleDeep learning methods for solar fault detection and classification: A reviewen_US
dc.typeArticleen_US
dspace.entity.typePublication
Files
Collections
SCOPUS