New improved hybrid MPPT based on neural network-model predictive control-kalman filter for photovoltaic system

Abstract

<p>In this paper, new hybrid Maximum Power Point Tracking strategy for<br />Photovoltaic Systems has been proposed. The proposed technique for<br />control based on a novel combination of an Artificial Neural Network with<br />an improved Model Predictive Control using Kalman Filter . In this paper the<br />Kalman Filter is used to estimate the converter state vector for minimized the<br />cost function then predict the future value to track the Maximum Power<br />Point with fast changing weather parameters. The proposed control<br />technique can track the in fast changing irradiance conditions and a small<br />overshoot. Finally, the system is simulated in the MATLAB/Simulink<br />environment. Several tests under stable and variable environmental<br />conditions are made for the four algorithms, and results show a better<br />performance of the proposed compared to conventional Perturb and<br />Observation Neural Network based Proprtional Integral Control and Neural<br />Network based Model Predictive Control in terms of response time,<br />efficiency and steady-state oscillations.</p>