This paper presents a control scheme for the optimization
of the efficiency of a grid-connected hybrid generation
system consisting of a photovoltaic generator and a wind turbine.
The design of the control system is made using a Xilinx System
Generator tool that allows the future implementation of the code
in a Field-Programmable Gate Array board. An online-trained
Artificial Neural Network-based control scheme has been used
in order to improve the performance of the classical control
algorithms. A recurrent Elman Neural Network and a Feed
Forward Neural Network have been chosen in order to maximize
the power produced by the two renewable energy-based sources.
Furthermore, the supervision of the grid-connected inverter is
ensured by means of a traditional Voltage Oriented Control
scheme. The simulation results, that have been obtained in a
Matlab/Simulink environment, prove the effectiveness and the
accuracy of the developed control system.
