SOLAR CELL SURFACE DEFECT DETECTION USING DEEP LEARNING

Abstract

Solar energy is a leading renewable energy source, and the efficiency of solar panels largely depends on the condition of the solar cell surface. Surface defects such as cracks, scratches, dust accumulation, hotspots, discoloration, and micro-fractures can significantly reduce energy conversion efficiency and shorten the lifespan of solar cells. Early detection of these defects is crucial for optimal performance and durability. This project focuses on Solar Cell Surface Detection using image processing and machine learning techniques. High-resolution images of solar cell surfaces are captured using cameras or drone-based inspection systems. The images undergo preprocessing to remove noise and enhance clarity. Image processing techniques such as edge detection, thresholding, segmentation, and feature extraction are applied to identify surface irregularities. Advanced systems use deep learning models, including Convolutional Neural Networks (CNNs), to automatically classify defects with high accuracy. The proposed system reduces manual inspection efforts, minimizes maintenance costs, and improves the reliability of solar power plants. Automated detection allows faster fault identification and prevents power losses caused by unnoticed defects. This approach is efficient, accurate, and scalable for both large solar farms and rooftop installations.