A novel particle swarm optimization optimal control parameter determination strategy for maximum power point trackers of partially shaded photovoltaic systems

Abstract

This article introduces a novel strategy for determining the optimal control parameters of particle swarm optimization (PSO) for the shortest convergence time and lowest failure rate of photovoltaic (PV) maximum power point tracker (MPPT) systems. This strategy is used offline to determine these parameters and then the control system uses them in the online MPPT. The strategy uses two nested particle swarm optimization (NESTPSO) search loops: the inner one involves the PV system and the outer one uses the inner PSO as a fitness function. The control parameters and swarm size of the inner PSO loop are used as optimization variables in the outer PSO loop. This strategy can be used not only for PSO but also for all other optimization techniques. The simulation and experimental results obtained using the NESTPSO strategy show a great reduction of 77–681% in convergence time and failure rate compared to 10 benchmark strategies, proving the superiority of this technique.