To ensure the continuous and efficient operation of photovoltaic (PV) solar energy systems, it is critical to identify and remedy faults promptly. Early detection and accurate diagnosis of faults in solar panels help prevent system degradation and minimize the risk of severe consequences, such as material damage, safety hazards, or even fires in solar fields. Various approaches are available for identifying and characterizing faults and degradation in PV systems. Current research explores these methods, with some reviews focusing on fault classification, others on detection techniques, and some on the application of artificial intelligence models for fault diagnosis. In this context, this paper provides a comprehensive review of the types of faults that occur in solar panels, alongside both traditional and advanced fault detection methods, with particular emphasis on artificial intelligence-driven solutions. Bringing these various approaches together, our work highlights key aspects of effective fault detection and management in photovoltaic systems.
Rahmouni et al. (Mon,) studied this question.