Solar Panel Fault Diagnosis Using Image Analysis Techniques

Abstract

Solar energy plays a critical role in meeting the growing demand for sustainable power; however, the performance of photovoltaic panels is often degraded by faults such as cracks, dust accumulation, shading, bird droppings, corrosion, and other surface defects. Undetected faults can significantly reduce energy output, decrease panel lifespan, and increase maintenance costs. Conventional inspection methods are labor-intensive, time-consuming, and often unsuitable for large-scale deployment. This paper presents a low-cost, real-time solar panel fault detection system implemented entirely on the Jetson Nano. The proposed system utilizes RGB image acquisition, where images of solar panels are captured under varying environmental conditions. The Jetson Nano performs all processing tasks locally, including image preprocessing, feature extraction, and fault identification. Preprocessing steps such as resizing, noise reduction, and contrast enhancement are applied to improve image quality and ensure reliable analysis. The device then analyzes visual patterns such as textures, edges, and surface variations to identify and classify different types of faults. The system operates as an edge-based solution, eliminating the need for cloud infrastructure, thereby reducing latency and enabling deployment in remote locations. Experimental results demonstrate that the proposed system achieves high detection accuracy, exceeding 90% for most fault categories, while maintaining an average processing time of less than one second per image. The system also shows strong reliability with minimal false detections. Compared to traditional inspection methods, the proposed approach significantly reduces maintenance effort and operational costs. Furthermore, it contributes to improved energy efficiency and supports sustainable practices by enabling early fault detection. The results indicate that the Jetson Nano provides a practical, scalable, and efficient platform for real-time solar panel monitoring and fault diagnosis.