A Fast GMPPT Scheme Based on Collaborative Swarm Algorithm for Partially Shaded Photovoltaic System

Abstract

Partial shading condition (PSC) is an inevitable issue faced by the photovoltaic (PV) modules, where the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$I$ </tex-math></inline-formula> – <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$V$ </tex-math></inline-formula> curve shows multiple current stairs and the ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$P$ </tex-math></inline-formula> – <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$V$ </tex-math></inline-formula> ) curve exhibits several peaks. In this article, a collaborative swarm algorithm (CSA) scheme to tackle the PSCs has been proposed. The intelligent mechanism of the CSA method allows the share of the best solution ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$G_{\mathrm {best}}$ </tex-math></inline-formula> ) found between each algorithm dynamically, which diversifies the exploration phase and helps to determine the accurate global maximum power point (GMPP). The proposed method is deterministic as the utilization of random numbers has been avoided, and only two tuning parameters require tuning. A buck–boost converter is used to verify the proposed method experimentally. The results show that the average tracking time is significantly improved by 200% compared with particle swarm optimization (PSO), Jaya, and ant colony optimization based on new pheromone update (ACO-NUP) algorithms and by 85% compared with the ACO-NUP-PSO algorithm. The collaborative approach was found to be superior over the utilization of individual types of metaheuristic algorithms in terms of tracking speed and efficiency.