@inproceedings{e151233c48be4ee683d22a0ed10d54bf,
title = "ANN-PSO Optimization of PV Systems under Different Weather Conditions",
abstract = "Conventional maximum power point tracking (MPPT) techniques like Perturb 0bserve perform ineffectively under partial shading condition due to its inability to effectively track the global maximum power point (GMPP). Particle swarm optimization (PSO) is a recent metaheuristic technique and has been used to extract maximum power from PV systems but takes time to iteratively locate the GMPP. This paper presents a novel hybrid ANN-PSO technique using series-connected distributed MPPT configuration. The simulation MPPT results using ANN- PSO configuration, PSO, and Perturb observe (P O) were compared with theoretical power values under different weather conditions to determine the most efficient MPPT method that can be considered for MPPT task in PV systems under uniform irradiance and partial shading conditions. Obtained results show that ANN-PSO can achieve the best performance.",
keywords = "ANN-PSO, Distributed MPPT, Global MPPT, MPPT, P O, PSO, Partial shading, Photovoltaic systems",
author = "Farayola, {Adedayo M.} and Yanxia Sun and Ahmed Ali",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 7th International IEEE Conference on Renewable Energy Research and Applications, ICRERA 2018 ; Conference date: 14-10-2018 Through 17-10-2018",
year = "2018",
month = dec,
day = "6",
doi = "10.1109/ICRERA.2018.8566974",
language = "English",
series = "7th International IEEE Conference on Renewable Energy Research and Applications, ICRERA 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1363--1368",
booktitle = "7th International IEEE Conference on Renewable Energy Research and Applications, ICRERA 2018",
address = "United States",
}
