Hardware Implementation of Hybrid Wind-Solar Energy System for Pumping Water Based on Artificial Neural Network Controller

Ons ZARRAD^1*, Mohamed Ali HAJJAJI^2,3, Mohamed Nejib MANSOURI¹
¹ Unit of Industrial Systems Study and Renewable Energy, National Engineering School, University of Monastir, Monastir 5000, Tunisia
Zarrad_ons@yahoo.fr (^*Corresponding author), mansouri.nejib@hotmail.fr
² Laboratory of Electronic and Microelectronic, University of Monastir, Monastir 5000, Tunisia
³ Higher Institute of Applied Sciences and Technology of Kasserine, University of Kairouan, Kairouan 3100, Tunisia
daly_fsm@yahoo.fr

ABSTRACT: Solar energy and wind energy are being used more and more as a renewable source by various countries for different purposes. These energies offer many advantages and have a unique limitation due to the instability of energy. The aim of this paper is to command and synchronize the power flow of one hybrid system using two sources of energy (solar and wind). The first contribution of the present work is represented by the utilization of an Artificial Neural Network controller to command the maximum power point at fixed atmospheric conditions. The second contribution is represented by the optimization of the system respecting real-time constraints in order to increase the generating system performance. For this, the simulation and hardware implementation of the proposed algorithm are accomplished using MATLAB/SIMULINK and a Xilinx System Generator. The simulation results confirm that the considered system presents acceptable execution real time performance and precision. The proposed designed model and its control strategy give the opportunity to optimize the hybrid power system performance, which is utilized in rural pumping applications.

KEYWORDS: Wind turbine system, PV, MPPT, Artificial Neural Network controller, FPGA, Pumping water.

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CITE THIS PAPER AS:
Ons ZARRAD, Mohamed Ali HAJJAJI, Mohamed Nejib MANSOURI, Hardware Implementation of Hybrid Wind-Solar Energy System for Pumping Water Based on Artificial Neural Network Controller, Studies in Informatics and Control, ISSN 1220-1766, vol. 28(1), pp. 35-44, 2019. https://doi.org/10.24846/v28i1y201904