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• W4313444782 abstract "Fault identification in Photovoltaic (PV) panels is of prime importance during the regular operation and maintenance of PV power plants. An extensive fault identification process that employs Image Processing, Machine Learning, and Electrical-based techniques has been analyzed comprehensively. Photovoltaic panels are the perfect choice of renewable energy from natural light sources. The energy yield of PV panel is degraded gradually because of dust, discoloration, crack and faults. These faults attribute a production loss of up to 20%, as one faulty panel can bring down the efficiency of the entire plant. It is implied that faulty panels need to be identified as soon as possible, and it is highly critical to resolving issues to ensure effective energy production. The various IP techniques and ML models that elucidate ways to identify PV panel defects have been addressed. Inferences made from this study to help identify three methods for defect detection that stand apart in terms of efficiency. Parametric observations on all three methods are made in terms of F1 Score, Precision, Accuracy and Recall. Numerical values indicate clearly that the Machine Learning method based on AlexNet predicts the Photovoltaic panel’s defect with 0.86 F1 value, 0.89 precision and an accuracy of 85.56%." @default.
• W4313444782 created "2023-01-06" @default.
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• W4313444782 date "2023-01-01" @default.
• W4313444782 modified "2023-09-23" @default.
• W4313444782 title "Comprehensive Analysis of Defect Detection Through Image Processing and Machine Learning for Photovoltaic Panels" @default.
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• W4313444782 doi "https://doi.org/10.1007/978-981-19-7169-3_23" @default.
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