Open AccessNew improved hybrid MPPT based on neural network-model predictive control-kalman filter for photovoltaic system
Author(s) -
Nora Kacimi,
Said Grouni,
Abdelhakim Idir,
Mohamed Seghir Boucherit
Publication year - 2020
Publication title -
indonesian journal of electrical engineering and computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.241
H-Index - 17
eISSN - 2502-4760
pISSN - 2502-4752
DOI - 10.11591/ijeecs.v20.i3.pp1230-1241
Subject(s) - control theory (sociology) , overshoot (microwave communication) , artificial neural network , kalman filter , model predictive control , maximum power point tracking , photovoltaic system , filter (signal processing) , engineering , computer science , power (physics) , control (management) , artificial intelligence , physics , telecommunications , electrical engineering , quantum mechanics , inverter
In this paper, new hybrid Maximum Power Point Tracking strategy for Photovoltaic Systems has been proposed. The proposed technique for control based on a novel combination of an Artificial Neural Network with an improved Model Predictive Control using Kalman Filter . In this paper the Kalman Filter is used to estimate the converter state vector for minimized the cost function then predict the future value to track the Maximum Power Point with fast changing weather parameters. The proposed control technique can track the in fast changing irradiance conditions and a small overshoot. Finally, the system is simulated in the MATLAB/Simulink environment. Several tests under stable and variable environmental conditions are made for the four algorithms, and results show a better performance of the proposed compared to conventional Perturb and Observation Neural Network based Proprtional Integral Control and Neural Network based Model Predictive Control in terms of response time, efficiency and steady-state oscillations.