Metaheuristic optimization based fault diagnosis strategy for solar photovoltaic systems under non-uniform irradiance

Solar energy is acquiring large global share in renewable energy sector. Open and short circuited faults in SPV system are major degrading factors for solar power production. In this scenario, proficient fault diagnosis strategies for productive operation of SPV system are gaining essentiality. In this work, a novel approach to employ metaheuristic optimization as a fault diagnosis technique for SPV systems has been proposed. The technique is able to identify, locate and differentiate among the open and short circuited modules in an SPV array under non-uniform irradiance and temperature distribution. The power output of SPV array is analyzed for diagnosis purpose. The generated power of the SPV array is estimated using a Matlab simulation model, which is compared with the physically measured power output. Improved Real Coded Genetic Algorithm, a mathematical optimizer, is employed here to predict the probable fault pattern which causes the simulated power output to be same as the physically measured power. For validation of the proposed methodology, the diagnosis approach is implemented in an SPV test system in laboratory. The obtained results show that the proposed method can accurately detect, locate and differentiate among the open circuit and short circuit faults in an SPV array.