Experimental control of photovoltaic system using neuro – Kalman filter maximum power point tracking (MPPT) technique

This paper proposes a new maximum power point tracking (MPPT) technique of photovoltaic system based on Kalman filter (KF) and associate to Artificial Neural Networks (ANN). The design process of photovoltaic (PV) modules can be greatly enhanced by using advanced and accurate models. Furthermore, the use of a neural model especially for accuracy improvement of the electrical equivalent circuit parameters, where the analytic equation of the model cannot be easily expressed, because the relationship between parameters is nonlinear. The proposed neural network is trained once by using some measured I-V and P-V curves and to keep in account the change of all the parameters at different operating conditions. For that reason, to get the fast tracking performance on this noisy conditions, and to maximize the power of photovoltaic system a KF method have been used. The performance analysis of perturb and observe (P&O) and KF MPPT techniques has been simulated in MATLAB/Simulink software and their model and control schemes has been analyzed and validated.