CNN-Based Photovoltaic Fault Detection and Classification Using Deep Learning

The rapid growth of photovoltaic (PV) power generation systems has increased the need for intelligent fault detection techniques to improve system reliability, safety, and energy efficiency. Conventional fault diagnosis approaches often suffer from low accuracy and poor adaptability under varying environmental conditions. This paper presents a Convolutional Neural Network (CNN)-based photovoltaic fault detection and classification system for identifying multiple PV panel fault conditions, including electrical damage, bird-drop contamination, dusty panels, and clean operating states. The proposed model utilizes deep learning techniques to automatically extract important fault features from solar panel images without manual feature engineering. Data preprocessing and normalization techniques are employed to improve training efficiency and classification performance. The CNN model is trained and validated using a photovoltaic image dataset under different environmental conditions. Experimental results demonstrate that the proposed model achieves approximately 99% training accuracy and 82% validation accuracy with a validation loss of 0.63. The model successfully classifies most photovoltaic fault categories with high reliability while maintaining stable convergence during training. The obtained results confirm that the proposed deep learning framework provides an efficient solution for real-time photovoltaic fault monitoring, predictive maintenance, and intelligent renewable energy management systems.