Optimization of fuzzy-based MPPT controller via reptile search algorithm for stand-alone PV systems

The performance of photovoltaic (PV) systems is highly dependent on the accurate tracking of the maximum power point (MPPT) of the system because the efficiency of the system is maximized in this way. MPP depends on the environmental conditions and changes with changes in temperature, radiation and partial shade. Therefore, various methods for MPPT have been proposed and improved, but not all the challenges of these systems such as power ripples, undesirable efficiency and low convergence speed have been completely resolved. In this research, a new fuzzy logic controller (FLC) for MPPT is introduced, and the reptile search algorithm (RSA) is used to optimize the performance of this controller. The performance of the proposed RSA-FLC MPPT method is compared with several modern algorithms, including the cuckoo optimization algorithm (COA) with fuzzy controller, which is one of the most efficient methods in parameter optimization, and the particle swarm optimization (PSO) algorithm with fuzzy controller (PSO-FLC), the standard fuzzy controller (FLC), as well as the traditional incremental conductance (InC) algorithm and a PV system without MPPT. The simulation results performed in the MATLAB/Simulink environment show that the proposed method increases the system efficiency in a highly optimal manner under all dynamic conditions, including changes in radiation, temperature, and even variable loads, as well as in partial shade conditions.