A comparative study on bayes classifier for detecting photovoltaic module visual faults using deep learning features

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dc.authoridS, Naveen Venkatesh/0000-0002-4034-8859
dc.authorwosidS, Naveen Venkatesh/GNM-5892-2022
dc.contributor.authorVenkatesh, S. Naveen
dc.contributor.authorSugumaran, V.
dc.contributor.authorSubramanian, Balaji
dc.contributor.authorJosephin, J. S. Femilda
dc.contributor.authorVaruvel, Edwin Geo
dc.date.accessioned2024-05-19T14:40:22Z
dc.date.available2024-05-19T14:40:22Z
dc.date.issued2024
dc.departmentİstinye Üniversitesien_US
dc.description.abstractRenewable energy is found to be an effective alternative in the field of power production owing to the recent energy crises. Among the available renewable energy sources, solar energy is considered the front runner due to its ability to deliver clean energy, free availability and reduced cost. Photovoltaic (PV) modules are placed over large geographical regions for efficient solar energy harvesting, making it difficult to carry out maintenance and restoration works. Thermal stresses inherited by photovoltaic modules (PVM) under varying environmental conditions can lead to failure of internal components. Such failures when left undetected impart a number of complications in the system that will lead to unsafe operation and seizure. To avoid the aforementioned uncertainties, frequent monitoring of PVM is found necessary. The fault identification in PVM using essential features taken from aerial images is presented in this study. The feature extraction procedure was carried out using convolutional neural networks (CNN), while the feature selection process was carried out by the J48 decision tree method. Six test conditions were considered such as delamination, glass breakage, discoloration, burn marks, snail trail, and good panel. Bayes Net (BN) and Naive Bayes (NB) classifiers were utilized as primary classifiers for all the test conditions. Results obtained from the classifiers were compared and the best classifier for fault detection in PVM is suggested.en_US
dc.identifier.doi10.1016/j.seta.2024.103713
dc.identifier.issn2213-1388
dc.identifier.issn2213-1396
dc.identifier.scopus2-s2.0-85186546512en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org10.1016/j.seta.2024.103713
dc.identifier.urihttps://hdl.handle.net/20.500.12713/4949
dc.identifier.volume64en_US
dc.identifier.wosWOS:001206468100001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofSustainable Energy Technologies and Assessmentsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240519_kaen_US
dc.subjectCondition Monitoringen_US
dc.subjectPhotovoltaic Modules (Pvm)en_US
dc.subjectFault Diagnosisen_US
dc.subjectMachine Learningen_US
dc.subjectConvolutional Neural Networks (Cnn)en_US
dc.subjectVisual Faultsen_US
dc.subjectFeature Extractionen_US
dc.titleA comparative study on bayes classifier for detecting photovoltaic module visual faults using deep learning featuresen_US
dc.typeArticleen_US

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