Design of Neural Network Fed MPPT Controller for Enhancing the Efficiency of Wind Power Network

Due to the destructive effects of natural fossil fuel power plants on nature, Alternative Sources of ENERGY (AES) are becoming increasingly important in the world's electrical energy production. Additionally, the supply of fossil fuels will diminish. Sun, wind, hydropower, and tidal energy are Renewable Energy Sources (RER), the main sources of energy. The rate of energy harvesting has accelerated in wind and solar Photovoltaic (PV) power facilities. Given that wind and sunlight are both abundant in nature, natural resources, on the other hand, are seasonal and vary depending on the weather. As a result, solar and wind power producers create erratic electrical energy that compromises their stability. The Maximum Power Point Tracking (MPPT) approach helps correct it. Currently, the MPPT technology is used with RER to produce the most electrical energy possible from the given resources. To examine the importance of MPPT, a wind energy system utilizing a Radial Basis Functional Controller (RBFC) is used for obtaining the maximum power of the system. The simulation findings demonstrate that wind generators can produce continuous power when using an MPPT technique based on RBFC. The precisely built boost converter also greatly increases the wind power output.