A novel MPPT control design for PV modules using neural network compensator

In this study, based on the slope of power versus voltage, a novel MPPT algorithm using neural network compensator is proposed to avoid the oscillation problem and effect of uncertain parameters in photovoltaic (PV) systems. However, the characteristics of PV output voltage and output current are determined by the solar irradiation conditions, ambient temperature, and the load electrical characteristics, thereby the technologies of changing the location of the maximum power point must be developed in the applications of maximum-power-point-tracking (MPPT) control in order to make the PV arrays get the optimal efficiency from solar energy at different operating conditions. In this study, the uncertainties in PV systems are compensated by a neural network and the duty cycle of dc/dc converter is determined by a PI controller. The control objectives of this study is to achieve MPPT for the PV systems including solar cell arrays, a dc/dc converter, and an output load despite the variation of solar irradiation, ambient temperature, and the load electrical characteristics in PV systems. From experimental results, the validity of the proposed MPPT controller can be verified under a certain solar irradiation and a partially shaded condition, respectively.