Analyzing Photovoltaic System Performance through Two Phase Interleaved Boost Converter and Particle Swarm Optimization-Based Maximum Power Point Tracking

Abstract

Photovoltaic (PV) systems, which convert solar energy into electricity through irradiation, play a crucial role in renewable energy generation. To ensure these systems operate efficiently and maximize power output, Maximum Power Point Tracking (MPPT) is essential. Numerous algorithms have been developed to optimize MPPT, one of which is the metaheuristic Particle Swarm Optimization (PSO) inspired by swarm intelligence. Boost converters are used to increase and regulate the voltage and current output of PV panels, ensuring they operate at the MPP under varying irradiation and temperature conditions. The synergy between boost converters and PSO-based MPPT has been examined, revealing significant potential in enhancing energy harvesting efficiency. Simulations and experimental validations demonstrate that the proposed integration outperforms traditional MPPT techniques. This article presents a comprehensive study on the implementation of a boost converter and the use of the PSO algorithm for MPPT in PV systems. The research contributes to the sustainable use of solar energy resources by enhancing the reliability and efficiency of PV systems. The findings offer substantial insights into the intricate design and optimization processes of photovoltaic installations. These insights are pivotal in advancing the efficiency and reliability of PV systems, which in turn, facilitate the broader adoption of renewable energy technologies. By addressing critical aspects of PV system performance and improving energy harvesting capabilities, this research accelerates the adoption of renewable energy, supports global sustainability efforts, and promotes a cleaner, greener energy landscape.