TOWARDS EFFICIENT SOLAR PANEL FAULT DETECTION THROUGH NEURAL NETWORK MODELING

Abstract

Solar power is a clean, renewable energy source with minimal greenhouse gas emissions, playing a vital role in combating climate change and enhancing energy self-sufficiency. Early fault detection, such as shading, cracking, or electrical malfunctions, is crucial for maintaining maximum efficiency and preventing system failures. This work presents a deep learning model based on the ResNet50 architecture, designed for the identification of faults caused by contaminants on the surface of solar panels. ResNet50, with its residual learning framework and deep hierarchical feature extraction, enables effective handling of vanishing gradient problems while capturing fine-grained patterns in solar panel images. Its skip connections improve learning efficiency and robustness, making it suitable for detecting subtle surface abnormalities under diverse environmental conditions. The proposed model is trained on a comprehensive dataset of clean and faulty solar panels under various weather scenarios, ensuring robustness against variations in lighting, dust accumulation, and physical damage. Unlike traditional machine learning methods that require handcrafted features or additional classifiers, the ResNet50-based approach directly performs end-to-end learning, enhancing detection efficiency. This study highlights the potential of ResNet50 to provide a robust, efficient, and automatic solution for solar panel fault detection. By enabling reliable identification of surface defects, the proposed system contributes to improved maintenance strategies and long term reliability of solar energy systems.