This study presents a defect classification method using the k-nearest neighbors (kNN) algorithm, optimized with current-voltage curves. This method identifies three specific faults: Partial shading, shunted modules, and ground faults. . This study explores the potential of using infrared solar module images for the detection of photovoltaic panel defects through deep learning, which represents a crucial step toward enhancing the efficiency and sustainability of solar energy systems. A dataset comprising 20,000 images, derived from. . Photovoltaic (PV) panels can experience various defects due to operational conditions, environmental factors, or human errors, leading to performance degradation and general risks such as system failures, inefficiencies, and potential fire hazards.
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