Analysis of a Solar PV Source fed Two-Stage Converter with Intelligent Control for Single-Phase Grid-Connected Systems

Maximum Power Point Tracking (MPPT) for a grid tied solar photovoltaic (PV) system, employing a machine learning-based Artificial Neural Network (ANN) is proposed. This method utilizes a Luo converter to boost the output of pv system while maintaining the converter system output voltage within acceptable limits. Additionally, a single-phase grid system is integrated, allowing flexible operation at arbitrary power factors, with a preference for unity power factor to maximize inverter capacity utilization. The machine learning-based ANN MPPT algorithm is designed to effectively trail the MPP of solar system, particularly in scenarios involving partial shading conditions. Advanced tracking mechanism ensures optimal energy harvesting even in challenging environmental conditions, enhancing the overall system efficiency and reliability of solar PV sourced converter system. Integration of a single-phase grid system further enhances the versatility of the setup, accommodating operation at different power factors to meet specific grid requirements. The results obtained from the simulations will provide insights into the system's robustness, efficiency, and its ability to adapt to real-world operating conditions.