Venkatesh, S. NaveenSugumaran, V.Subramanian, BalajiJosephin, J. S. FemildaVaruvel, Edwin Geo2024-05-192024-05-1920242213-13882213-1396https://doi.org10.1016/j.seta.2024.103713https://hdl.handle.net/20.500.12713/4949Renewable 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.eninfo:eu-repo/semantics/closedAccessCondition MonitoringPhotovoltaic Modules (Pvm)Fault DiagnosisMachine LearningConvolutional Neural Networks (Cnn)Visual FaultsFeature ExtractionArticle64WOS:0012064681000012-s2.0-85186546512N/A10.1016/j.seta.2024.103713Q1