This paper presents a robust framework for detecting faults in PV panels using Convolutional Neural Networks (CNNs) for feature extraction and Bitterling Fish Optimization (BFO) algorithm for feature selection. The system integrates five pre-trained CNN architectures—GoogleNet, SqueezeNet. . Photovoltaic panel defect detection presents significant challenges due to the wide range of defect scales, diverse defect types, and severe background interference, often leading to a high rate of false positives and missed detections. To address these challenges, this paper proposes the. . f power output from solar power plants. This study focuses on the detection and monitoring of sand deposition (wind-blown dust) on photovoltaic (PV) solar panels in arid regions sing multitemporal remote sensing data. The study area is located in Bhadla solar par jec 46-1:2016 Photovoltaic (PV). .
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