Improved Solar Photovoltaic Array Model with FLC Based Maximum Power Point Tracking

This paper presents an improved model of solar photovoltaic (PV) array along with the implementation of fuzzy logic as maximum power point tracking (MPPT). The proposed PV array behavioral model is more accurate and with reduced complexity though considered discrete components. The PV array model was well verified by considering the effect of change of environmental conditions, mainly intensity of solar irradiation (insolation) and temperature. The model was tested by feed a single phase inverter. MPPT control the operating voltage of PV arrays in order to maximize their power output as a result maximize the array efficiency and minimize the overall system cost. Using a Fuzzy logic based algorithm, the duty cycle of the converter inserted between source and load is adjusted continuously to track the MPP and compared with the conventional perturb and observed (P&O) method for changing environmental conditions. It was found that the Fuzzy logic based method can track the MPP more precisely and rapidly than the conventional one. In P&O method, if step size of input variable is very small, the accuracy in tracking MPP is sufficient but tracking speed becomes too slow. On the other hand if the step size is increased to imitate the rapidly changing weather conditions, accuracy deteriorates and unexpected results occur due to oscillation around a mean point although tracking speed increased. But in the case of proposed FLC whatever the step size of input variable it best suited to track MPP continuously and accurately. The obtained simulation results validate the competent of the solar PV array model as well as the fuzzy controller.