Deep learning based fault detection and diagnosis in photovoltaic system using thermal images acquired by UAV

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dc.authoridDEMIR, Batikan Erdem/0000-0001-6400-1510
dc.contributor.authorKayci, Baris
dc.contributor.authorDemir, Batikan Erdem
dc.contributor.authorDemir, Funda
dc.date.accessioned2024-09-29T16:06:49Z
dc.date.available2024-09-29T16:06:49Z
dc.date.issued2024
dc.departmentKarabük Üniversitesien_US
dc.description.abstractSolar power is one of the largest renewable energy sources in the world. With photovoltaic systems, electrical energy can be generated wherever the sun is located. To prevent efficiency losses in photovoltaic systems, these systems should be tested at regular intervals. In this study, it is discussed to detect cell, module and panel faults in panels using thermal images obtained from solar panels. Within the scope of the study, a four-rotor unmanned aerial vehicle (drone) was designed and a thermal camera was placed on the vehicle. Thus, thermal images of the solar panels on the roof of Karabuk University buildings were taken. A thermal data set with cell fault, module fault and panel fault were created using the resulting thermal images. The YOLOv3 deep learning-based convolutional neural network was trained with the created dataset. This training was conducted on Nvidia Jetson TX2, an embedded AI (Artificial Intelligence) computing device. After the completion of the training of the YOLOv3 network, it was concluded that the faults mentioned in the tests were successfully detected.en_US
dc.description.sponsorshipKarabuk University [FYL2019-2131]en_US
dc.description.sponsorshipThis study was supported by Karabuk University within the scope of Scientific Research Projects with FYL2019-2131 code.en_US
dc.identifier.doi10.2339/politeknik.1094586
dc.identifier.issn1302-0900
dc.identifier.issn2147-9429
dc.identifier.issue1en_US
dc.identifier.trdizinid1227709en_US
dc.identifier.urihttps://doi.org/10.2339/politeknik.1094586
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay/1227709
dc.identifier.urihttps://hdl.handle.net/20.500.14619/7062
dc.identifier.volume27en_US
dc.identifier.wosWOS:000831309700001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakTR-Dizinen_US
dc.language.isoenen_US
dc.publisherGazi Univen_US
dc.relation.ispartofJournal of Polytechnic-Politeknik Dergisien_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectPhotovoltaic systemen_US
dc.subjectthermographyen_US
dc.subjectdeep learningen_US
dc.subjectfault detection and diagnosisen_US
dc.subjectunmanned aerial vehiclesen_US
dc.titleDeep learning based fault detection and diagnosis in photovoltaic system using thermal images acquired by UAVen_US
dc.typeArticleen_US

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