An efficient fault classification method in solar photovoltaic modules using transfer learning and multi-scale convolutional neural network

dc.authorid0000-0002-5159-0659en_US
dc.contributor.authorKorkmaz, Deniz
dc.contributor.authorAçıkgöz, Hakan
dc.date.accessioned2022-07-22T11:36:47Z
dc.date.available2022-07-22T11:36:47Z
dc.date.issued2022en_US
dc.departmentMTÖ Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.descriptionReceived 4 January 2022, Revised 29 April 2022, Accepted 9 May 2022, Available online 21 May 2022, Version of Record 21 May 2022.en_US
dc.description.abstractPhotovoltaic (PV) power generation is one of the remarkable energy types to provide clean and sustainable energy. Therefore, rapid fault detection and classification of PV modules can help to increase the reliability of the PV systems and reduce operating costs. In this study, an efficient PV fault detection method is proposed to classify different types of PV module anomalies using thermographic images. The proposed method is designed as a multi-scale convolutional neural network (CNN) with three branches based on the transfer learning strategy. The convolutional branches include multi-scale kernels with levels of visual perception and utilize pre-trained knowledge of the transferred network to improve the representation capability of the network. To overcome the imbalanced class distribution of the raw dataset, the oversampling technique is performed with the offline augmentation method, and the network performance is increased. In the experiments, 11 types of PV module faults such as cracking, diode, hot spot, offline module, and other classes are utilized. The average accuracy is obtained as 97.32% for fault detection and 93.51% for 11 anomaly types. The experimental results indicate that the proposed method gives higher classification accuracy and robustness in PV panel faults and outperforms the other deep learning methods and existing studiesen_US
dc.identifier.citationKorkmaz, D., & Acikgoz, H. (2022). An efficient fault classification method in solar photovoltaic modules using transfer learning and multi-scale convolutional neural network. Engineering Applications of Artificial Intelligence, 113, 104959.en_US
dc.identifier.doi10.1016/j.engappai.2022.104959
dc.identifier.endpage14en_US
dc.identifier.issn0952-1976en_US
dc.identifier.scopus2-s2.0-85130461499en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage1en_US
dc.identifier.urihttps://doi.org/10.1016/j.engappai.2022.104959
dc.identifier.urihttps://hdl.handle.net/20.500.12899/1172
dc.identifier.volume113en_US
dc.identifier.wosWOS:000830168800013en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorKorkmaz, Deniz
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofEngineering Applications of Artificial Intelligenceen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectSolar energyen_US
dc.subjectPV modulesen_US
dc.subjectFault classificationen_US
dc.subjectConvolutional neural networken_US
dc.subjectTransfer learningen_US
dc.titleAn efficient fault classification method in solar photovoltaic modules using transfer learning and multi-scale convolutional neural networken_US
dc.typeArticleen_US

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