Research Article
On the effects of hyper-parameters adjustments to the PSO-GMPPT algorithm for a photovoltaic system under partial shading conditions
@ARTICLE{10.4108/eai.13-7-2018.160981, author={Andr\^{e} Luiz Marques Leopoldino and Cleiton Magalh\"{a}es Freitas and Lu\^{\i}s Fernando Corr\"{e}a Monteiro}, title={On the effects of hyper-parameters adjustments to the PSO-GMPPT algorithm for a photovoltaic system under partial shading conditions}, journal={EAI Endorsed Transactions on Energy Web}, volume={7}, number={25}, publisher={EAI}, journal_a={EW}, year={2019}, month={10}, keywords={Photovoltaic Energy Generation, Maximum Power Point Tracking, Particle Swarm Optimization}, doi={10.4108/eai.13-7-2018.160981} }- André Luiz Marques Leopoldino
Cleiton Magalhães Freitas
Luís Fernando Corrêa Monteiro
Year: 2019
On the effects of hyper-parameters adjustments to the PSO-GMPPT algorithm for a photovoltaic system under partial shading conditions
EW
EAI
DOI: 10.4108/eai.13-7-2018.160981
Abstract
This paper exploits the performance of the particle swarm optimization (PSO) algorithm for a photovoltaic system under partial shading condition (PSC). Essentially our main contribution consists on analyzing the hyper-parameters adjustment of the PSO algorithm to determine the minimum particle numbers, such that the assertiveness to identify the Global Maximum Power Point (GMPP) be higher than 99%. The database was obtained throughout 5760 simulations based on different test cases. From these test cases, the PSC was applied in 2880 simulations. In the previous work, it was shown the best results based on 5 particles. In this update version, it is also shown the best results for 3, 7 and 9 particles, together with a comparison among them. Furthermore, this paper also presents the simulation results to evaluate the performance of the developed algorithm under transient- and steady-state conditions.
Copyright © 2019 A. L. M. Leopoldino et al., licensed to EAI. This is an open assess article distributed under the terms of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.