By addressing real-world challenges in solar panel maintenance, the final dataset supports applications in automated defect detection, predictive maintenance, and energy optimization.
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Experimental results indicate that monocrystalline silicon panels have the lowest degradation rate, ranging from 0.861% to 0.886%, compared to thin-film panels, which range from
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Unlike surface-level assessments, EL imaging allows engineers to see inside the photovoltaic (PV) module itself. It allows them to identify microcracks, soldering defects, and
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This study introduces an automated defect detection pipeline that leverages deep learning and computer vision to identify five standard anomaly classes: Non-Defective, Dust,
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Photovoltaic (PV) fault detection and classification are essential in maintaining the reliability of the PV system (PVS). Various faults may occur in either DC or AC side of the
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In order to tackle this issue, this study presents a PV panel defect detection approach based on the advanced YOLOv11 object detection algorithm. The mosaic augmentation approach is first employed
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This study has comprehensively analysed the effects of hailstorms on photovoltaic (PV) modules, focusing on damage mechanisms, testing standards, detection methods, and strategies to
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In this study, we concentrate only on the techniques employed for the detection of faults on the DC side. Many researchers have suggested a number of diagnostic approaches specifically
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This study proposes a method for detecting and localizing solar panel damage using thermal images. The proposed method employs image processing techniques to detect and localize
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By integrating drone technology, the proposed approach aims to revolutionize PV maintenance by facilitating real-time, automated solar panel detection. This advancement promises substantial cost
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