On the Effects of Parameter Adjustment on the Performance of PSO-Based MPPT of a PV-Energy Generation System

Abstract

The growing concern on environmental issues caused by fossil fuels and, indeed, on the availability of such energy resources in a long-run basis have settled the ground for the spreading of the so called green energy sources. Among them, photovoltaic energy stands out due to the possibility of turning practically any household into a micro power plant. One important aspect about this source of energy is that practical photovoltaic generators are equipped with maximum power point tracking (MPPT) systems. Currently, researchers are focused on developing MPPT algorithms for partial shaded panels, among which, particle swarm optimization (PSO) MPPT stands out. PSO is an artificial intelligence method based on the behavior of flock of birds and it works arranging a group of mathematical entities named particles to deal with an optimization problem. Thus, this work focus on analyzing the performance of this algorithm under different design conditions, which means different amount of particles and different set points for the constants. Besides that, the article presents a brief guideline on how to implement PSO-MPPT. Simulations of an array with three photovoltaic panels, boost-converter driven, were carried out in order to back the analyzes.