The maximum power point tracking (MPPT) technique in the photovoltaic (PV) system is used to achieve maximum power from the solar PV system. In this context, three MPPT techniques, artificial neural network (ANN), fuzzy logic control (FLC) and adaptive neuro-fuzzy inference system (ANFIS), are implemented and their performance is investigated in terms of efficiency and response. And they are developed in MATLAB/Simulink environment. This system is developed by combining the models of established solar module and DC-DC boost converter with the genetic algorithm for the three techniques. So this paper presents a new approach based on the genetic algorithm used to perform a constrained tuning technique for the PID parameters to optimize the power output of solar panel. The dynamic model is used to design the controller parameters of the conventional PID controller. The dynamics of the DC-DC converter is non linear. Therefore, it is hard to derive desirable performance. Hence, Genetic algorithm is used to optimize the control parameters of the boost converter. In order to obtain the fitness of an individual, Simulink model of the boost converter is designed and the genetic algorithm is programmed to search for the optimal control parameters by the MATLAB built ingatool. The system is simulated under different climate conditions and MPPT algorithms. According to the comparisons of the simulation results, it can be observed that the photovoltaic simulation system can track the maximum power accurately using the three MPPT algorithms discussed. Therefore, the interest is generated to design a more effective and efficient MPPT to achieve maximum power transfer to the load.
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