Energy Management For Renewable Hybrid System Based On Artificial Neural Networks (ANN)

Abstract

Abstract: It has become necessary in recent years to use renewable energy sources, such as wind and solar energy, to meet the load demand. Because of their endless existence and environmentally friendly nature, they are useful for power generation. The management of energy in hybrid wind-solar power sources is the topic of this article. Using a common current source interface multiple-input DC-DC converter, the photovoltaic (PV) array, wind turbine, and battery storage are all connected. The power control between intermittent renewable energy generation, energy storage, and the grid is maintained by the artificial neural network (ANN) controller. For wind turbines to produce the most electricity, variable speed control can be used. For solar systems, the Maximum Power Point Tracking (MPPT) algorithm is used. By keeping the common DC voltage constant, the grid interface inverter sends the energy drawn from the PV array and wind turbine into the grid. Using MATLAB Simulink, the complete control strategy is subjected to a simulation study. The results of the simulation show the photovoltaic/wind hybrid system's dynamic behavior and control effectiveness