Combined approach between FLC and PSO to find the best MFs to improve the performance of PV system

During designing of fuzzy logic controller (FLC), an expert knowledge of the process to be controlled can be used to determine the membership functions (MFs) and the rules. However there is no general procedure for designing a FLc seen that many of errors may be encountered in its implementation, and these FLC can not be adapted to other applications. The difficulties encountered in the design of CLF have guided researchers to move towards the optimization of these controllers. The present paper proposes an approach combined from FLC and particle swarm optimization algorithm (PSO) used to finding the optimum membership functions (MFs) of a fuzzy system with the aim of achieving the accurate and acceptable desired results. For improving and optimizing the performance of a photovoltaic system to deliver the maximum power available. It is clearly proved that the optimized MFs provided better performance than a fuzzy model for the same system, when the MFs were heuristically defined.