Advanced Deep Learning Methods for Fault Detection in Photovoltaic Solar Panels: A Comparative Analysis

Detection of faulty photovoltaic solar panels is very important for maximum renewable energy production and maintaining operational efficiency.Several types of degradations may badly affect the performance of solar panels, like dust accumulation, snow cover, bird droppings, electrical damage, and physical damage.This article applies several advanced deep-learning models to accurately and speed up fault detection of solar panels.Preprocessing and data augmentation techniques were conducted to enhance the training samples.The models were trained to classify six fault categories: clean, dusty, snowy, bird droppings, electrical damage, and physical damage.To improve the model's accuracy further, a fine-tuning technique is applied to the VGG16 model to outperform other articles.The MobileNetV3 model achieves precise identification of faults with a validation accuracy of 93.22%, a test accuracy of 96.11%, and an F1 score of 0.96.In general, the proposed model outperforms in fault detection and classification activities.It could have many potential applications in realtime monitoring and maintenance of solar panels to maintain generation at an optimum.