New Improved hybrid MPPT based on Neural Network-Model Predictive Control- Kalman-Filter for Photovoltaic System

Nora Kacimi

Abstract


In this paper, new hybrid Maximum Power Point Tracking (MPPT) strategy for Photovoltaic Systems has been proposed. The proposed technique for MPPT control based on a novel combination of an Artificial Neural Network (ANN) with an improved Model Predictive Control using Kalman Filter (NN-MPC-KF). In this paper  the Kalman Filter is used to estimate the converter state vector for minimizes the cost function then predict the future value to track the Maximum Power Point (MPP) with fast changing weather parameters. The proposed control technique can track the MPP in fast changing irradiance conditions and a small overshoot. Finally, the system is simulated in the MATLAB/Simulink environment. The simulation results verify the appropriate performance of the proposed method. As a result, the proposed algorithm can achieve higher maximum power point tracking efficiency, faster dynamic response, and lower oscillations. In addition, the comparative simulation results of the proposed algorithm with the other maximum power point tracking algorithms show the superiority of the proposed algorithm.

Keywords


Artificial Neural Network; Model Predictive Control; Kalman Filter; Photovoltaic System; Proposed hybrid MPPT; Comparative study

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DOI: http://doi.org/10.11591/ijeecs.v20.i3.pp%25p
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