Artificial Intelligence for Fault Detection in Photovoltaic Panels

This paper presents an Artificial Intelligence solution for fault detection and classification in photovoltaic systems. The proposed tool integrates electrical and visual analysis methods, including I-V curve analysis, direct difference measurement, infrared thermography, electroluminescence imaging, and visual inspection. These methods are enhanced by deep learning models, which achieve high accuracy in identifying and diagnosing faults. A Python-based web application provides users with an intuitive interface for real-time data processing and fault classification. Experimental results demonstrate the tool's effectiveness, with neural network models achieving accuracy levels exceeding 98% in electrical methods and over 90% in visual methods. By optimizing fault detection processes, the tool reduces maintenance costs, minimizes downtime, and enhances the operational reliability of photovoltaic systems. This research represents a significant step toward scalable, automated maintenance solutions, ensuring photovoltaic systems' sustainability and efficiency in the transition to renewable energy.