LAUSR

Abstract With the elevated use of Solar Photovoltaic (PV) systems, efficiency improvement of PV systems has become a priority research topic nowadays. Moreover, remarkable efforts to study different PV fault diagnosis methods have been increasing to give the PV system efficiency researches an extra edge. In this study, meta-heuristic optimization techniques have been employed as a novel fault diagnosis methodology that can efficiently identify, locate and distinguish between open circuit (OC) and short circuit (SC) faults in a PV system. The diagnosis algorithm follows the string current considering the effect of non-uniform irradiance and corresponding module temperature. It has been experimented that the proposed methodology has achieved success in fault diagnosis in a physical test system and distinctly identify the fault locations and types like short circuit faults and open circuit faults in a PV string. Some well recognized algorithms like, Genetic Algorithm (GA), Tabu Search (TS) algorithm and a recently developed efficient optimization technique, namely Grey Wolf Optimization (GWO), have been used as optimizers in the proposed fault diagnosis methodology. Results and performances of these optimizers in the proposed fault diagnosis methodology have been compared and assessed for validation.