This study presents a deep learning-based approach for fault detection in solar panels, addressing the critical need for efficient maintenance in photovoltaic systems. Leveraging a comprehensive dataset of solar panel images, we classify six distinct conditions: Bird-drop, Clean, Dusty, Electrical-damage, Physical-Damage, and Snow-Covered. We evaluate multiple deep learning models, including Convolutional Neural Networks (CNN), DenseNet, MobileNetV3, VGG19, Inception V3, and RegNet, to identify the most effective architecture for accurate fault detection. The dataset, sourced from Kaggle, contains diverse images capturing solar panel conditions, enabling robust model training and evaluation. Each model is trained classification performance, with metrics such as accuracy, precision, recall, and F1-score used for comparison. Our results demonstrate that [model with best performance, e.g., DenseNet,MobilenetV3] achieves superior performance, offering high accuracy and robustness across varied fault types. This approach enhances automated monitoring, reducing manual inspection costs and improving the operational efficiency of solar energy systems. The study highlights the potential of deep learning in renewable energy maintenance, providing a scalable solution for fault detection. Future work will focus on real-time implementation and expanding the dataset for enhanced generalization.

Keywords: Solar Panel, Fault Detection, Deep Learning, CNN, DenseNet, MobileNetV3, VGG19, Inception V3, RegNet, Image Classification, Photovoltaic Systems, Renewable Energy.