Solar Panel Based Boost Converter with Neural Network for High Voltage Gain Applications

Solar panels with their low output voltage often face challenges in meeting the energy requirements of high-voltage gain applications. This project tackles these issues by developing a boost converter for solar panels, combined with a Maximum Power Point Tracking (MPPT) controller and a neural network-based control system. The neural network adjusts the converter's parameters dynamically, responding to variations in solar irradiation and load conditions to optimize system performance. This innovative approach achieves higher voltage gain, enhanced efficiency, and greater stability compared to traditional methods. The adaptability of the system makes it ideal for use in renewable energy applications and electric vehicles. Simulations conducted in MATLAB's Simulink environment demonstrate the system's ability to achieve MPPT while minimizing harmonics in the solar PV output. This ensures a reliable and efficient solution for scenarios requiring high-voltage gain with low input. The project underscores the role of artificial intelligence in improving the performance and reliability of solar energy systems.