Fault diagnosis and condition monitoring are important to increase the efficiency and reliability of photovoltaic modules. This paper reviews the challenges and limitations associated with diagnosing faults in solar modules. A thorough analysis of various faults responsible for failure of solar modules has been discussed.
One option, explored recently, is artificial intelligence (AI) to replace conventional maintenance strategies. The growing importance of AI in various real-life applications, especially in solar PV applications, cannot be over-emphasized. This study presents an extensive review of AI-based methods for fault detection and diagnosis in PV systems.
Analogously, it was argued that automated monitoring systems are significant for PV yield evaluation, and considerable losses can be avoided if fault detection models were put in place in industrial production plants.
The keywords used for the search were: Solar panel defect detection; PV module degradation; PV module fault detection, PV module degradation measurement methods, and techniques; Solar cell degradation detection technique; PV module, Solar panel performance measurement, PV module wastage, and its environmental effect, and PV module fault diagnosis.
The authors of [ 103] proposed an intelligent system for automatic fault detection in PV fields based on the Takagi-Sugeno-Kahn fuzzy rule-based system (TSK-FRBS) [ 104 ]. The method is based on the analysis of recorded voltages and currents collected from a PV plant’s inverter.
Moreover, maintenance staff will take more time and effort to fix undetermined faults. Due to the current-limiting nature and nonlinear output characteristics of PV arrays, fault detection is not that easy and the application of artificial intelligence is proposed for the sake of fault detection in PV systems.