Design and Experimental Validation of a Novel Particle Swarm Optimization Algorithm Designed to Optimize Solar Power Extraction

As the search for sustainable energy solutions increases, Photovoltaic (PV) panels have emerged as a crucial technology, harnessing solar energy to meet the growing global demand. These devices require maximum power point tracking (MPPT) for efficient operation as a consequence of their nonlinear electrical behavior. These nonlinearities cause traditional algorithms to be less than fully effective, thus creating room for improvement that can be filled by intelligent algorithm proposals, such as Particle Swarm Optimization (PSO). In this context, a new variant of the PSO algorithm based on evolutionary behavior and voltage window restrictions is presented, implemented, and validated with the aim of developing an advanced control system to operate in a real PV system for MPPT. The study covers several experiments comparing its performance with other PSO variants found in the literature. The proposed algorithm exhibits smoother transitions with fewer power shocks due to a restricted voltage window, ensuring rapid convergence through its evolutionary feature. These improvements lead to a significant reduction in energy losses during the search process, dropping from about 3.76% with the standard PSO to only 2.56%, while also halving the convergence time.