Implementation of Neural Network Controller for Grid Connected Wind-Solar PV Charging Station

This work proposes a novel approach to enhance grid-connected wind-solar PV charging stations that face challenges like fluctuating energy supply, inefficient resource usage, and the necessity for adaptive real-time control. Traditional control methods like PI controllers, often fall short in optimizing the system performance under the dynamic conditions, resulting in inadequate power supply for EV charging. To tackle these challenges, this study proposes a novel approach by employing Neural Network (NN) controllers to enhance grid-connected wind-solar PV charging stations' operation. NN controllers dynamically adjust charging station operations based on real-time data inputs, offering superior adaptability and efficiency. By integrating wind and solar power generation with intelligent NN control mechanisms, the system adeptly responds to varying environmental conditions and grid demands, ensuring more effective utilization of renewable energy sources. The proposed NN controller-based system targets enhancing the reliability, sustainability, and economic feasibility of grid-connected charging stations. Simulations showcase the effectiveness and stability of this approach in integrating renewable energy into transportation infrastructure. Performance evaluation can be conducted using Matlab/Simulink Software.