Paper Key : IRJ************028
Author: Sony Kumari
Date Published: 08 Jul 2024
Abstract
This study presents a Simulink-based simulation model designed to optimize the performance of renewable energy systems using neural network-based controllers. The model integrates two key controllers: a Maximum Power Point Tracking (MPPT) controller for solar photovoltaic (PV) systems and a pitch angle controller for wind farms. Neural networks are employed in both controllers to enhance their adaptive capabilities and improve efficiency under varying environmental conditions. The MPPT controller utilizes neural network algorithms to dynamically adjust the operating point of the solar PV array, ensuring maximum power extraction at all times. Similarly, the pitch angle controller employs neural networks to optimize the blade angle of wind turbines, thereby maximizing energy capture from wind resources while maintaining system stability. The simulation results demonstrate the effectiveness of the neural network-based controllers in enhancing the overall performance and reliability of renewable energy systems, highlighting their potential for real-world applications in sustainable energy generation.