A Comparative investigation of maximum power point tracking techniques for grid connected PV system under various weather conditions

The performance of the photovoltaic panel is significantly affected by various environmental factors, due to these changes there is little chance to operate the PV module at its maximum power point when using Perturb and Observe (P&O) algorithm. Hence, an intelligent controller is designed employing ANFIS which draws much energy and fast response. The current work proposes an artificial-intelligence based solution to interface a solar (PV) farm of 500 KW with utility grid. ANFIS systems are actually fuzzy inference systems tuned by neural networks. The ANFIS model is trained using randomly selected data obtained for various temperature and irradiation levels. The cell temperature and irradiation level are the input to the controller and the output of the considered controller is the duty cycle of boost DC-DC converter. Performance of this technique has been compared with perturb and observe (P&O) method of maximum power point tracking (MPPT) and simulation results showed the superiority of proposed method its simplicity and its high dynamic behavior within MATLAB/Simpower System Toolbox environment.