Fault Ranking in PV Module based on Artificial Intelligence Technique (AIT)

Abstract

Nowadays, focus on renewable energy sources (solar, wind, biogas etc.), especially on solar energy is to find the best alternative source of energy due to being hazardous free, pollution free, never end and abundant in nature etc. A photovoltaic (PV) systems (stand-alone, grid-connected or hybrid PV systems) consisting of many vulnerable components like module, connecting cable, fuse, diode, a power conditioning device etc., a fault in any components can lead to degradation of efficiency, energy output as well as the reliability of the overall PV systems, if not prior corrective action takes place. So, Fault detection and it's ranking for PV systems, especially focus on PV module, because it operates very harsh condition, plays a vital role for the system reliability and safety. In this research work, fault ranking in PV module has been done based on artificial intelligence (AIT) technique. Thus, fuzzy logic is applied to assess the critical fault in the PV module, according to their ranking. Fault possibilities in PV module are expressed by linguistic variables. A consistency agreement method technique has been used for aggregation of fuzzy number, assigned by the experts. The proposed method is best for ranking of occurrence of a fault in the PV module.