Modeling and control of grid connected intelligent hybrid photovoltaic system using new hybrid fuzzy-neural method

Photovoltaic (PV) system has non-linear current-voltage characteristics that generates maximum power at only one particular operating point. Irradiance and temperature variations have important role to affect the maximum power point (MPP). Diverse techniques have been introduced for tracking the MPP based on the offline and online methods. In this paper, in order to capture the maximum power, hybrid fuzzy-neural method is applied in PV system. Three case studies have implemented to show the effectiveness and superiority of the proposed method. It can be found that the hybrid fuzzy-neural controller can provide good dynamic operation, faster convergence speed, less oscillations of operating point around MPP, it tracks global maxima under different condition effectively than conventional methods. Operating point will not vary too much from MPP under quickly changing atmospheric condition and it is more effective and efficient as well as the average tracking efficiency of the hybrid fuzzy-neural is incremented by approximately two percentage points in comparison of the conventional methods. Detailed mathematical model and a control approach of a three-phase grid-connected intelligent hybrid system have proposed using Matlab/Simulink.