The use of ANN to supervise the PV MPPT based on FLC

This paper proposes an intelligent maximum power point tracking (MPPT) strategy based on the combination of fuzzy logic control (FLC) and artificial neural network (ANN) to improve the power output performances of PV system. Firstly, under various weather conditions, ANN is trained to estimate the optimal MPP voltage (V
_mpp
) of the PV module. Then, this trained ANN is used as a fundamental stage to drive the FLC based MPPT controller. The driven FLC is used to track the MPP by adjusting the duty cycle of the dc-dc converter of the PV system. The performances of the proposed tracking method are simulated and compared with the conventional perturb and observe (P&O) method at actual irradiation and temperature measurements using MATLAB simulation package. As it can be seen from the simulated results, the PV system with the proposed method is more efficient and can provide more energy, with less oscillation and overshoot, as compared with the conventional P&O MPPT method.