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In this paper, two main contributions are presented to manage the power flow between a wind turbine and a solar power system. The first one is to use the fuzzy logic controller as an objective to find the maximum power point tracking, applied to a hybrid wind-solar system, at fixed atmospheric conditions. The second one is to respond to real-time control system constraints and to improve the generating system performance. For this, a hardware implementation of the proposed algorithm is performed using the Xilinx System Generator. The experimental simulation results show that the suggested system presents high accuracy and acceptable execution time performances. The proposed model and its control strategy offer a proper tool for optimizing the hybrid power system performance which we can use in smart house applications.

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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.

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