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Fractional chaotic ensemble particle swarm optimizer for identifying the single, double, and three diode photovoltaic models� parameters

Heuristic algorithms; Particle swarm optimization (PSO); Photovoltaic cells; Renewable energy resources; Solar power generation; Commercial applications; Environmental conditions; Fast convergence rate; Meta heuristic algorithm; Optimization algorithms; Parameters estimation; Particle swarm optimize...

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Main Authors: Yousri D., Thanikanti S.B., Allam D., Ramachandaramurthy V.K., Eteiba M.B.
Other Authors: 56688582500
Format: Article
Published: Elsevier Ltd 2023
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spelling my.uniten.dspace-255392023-05-29T16:10:38Z Fractional chaotic ensemble particle swarm optimizer for identifying the single, double, and three diode photovoltaic models� parameters Yousri D. Thanikanti S.B. Allam D. Ramachandaramurthy V.K. Eteiba M.B. 56688582500 56267551500 55940454800 6602912020 6603527538 Heuristic algorithms; Particle swarm optimization (PSO); Photovoltaic cells; Renewable energy resources; Solar power generation; Commercial applications; Environmental conditions; Fast convergence rate; Meta heuristic algorithm; Optimization algorithms; Parameters estimation; Particle swarm optimizers; PV models; Diodes; algorithm; alternative energy; electrode; model; optimization; parameter estimation; photovoltaic system; temperature effect Solar Photovoltaic is a widely used renewable energy resource, and hence, the accurate and effective modeling of the PV system is crucial in real-time. The accurate PV modeling helps to predict the performance of the PV plant. In this paper, authors have proposed a novel optimization algorithm named Fractional Chaotic Ensemble Particle Swarm Optimizer (FC-EPSO) to model solar PV modules accurately. This article focused on the modeling of single, double, and three diodes models based on experimental data under different environmental conditions. In FC-EPSO, a new approach in the meta-heuristic algorithms is proposed, where fractional chaos maps are incorporated into the algorithm to enhance its accuracy and reliability. FC-EPSO variants performance is evaluated based on three-different experimental datasets, in which two are widely utilized for commercial applications, while the third is measured in the laboratory under four different irradiance and temperature levels. For validation purposes, several statistical analyses and comparisons are carried out with the recent state-of-the-art algorithms. The statistical measures and comparative studies illustrate the accuracy and consistency of the proposed algorithm. The introduced technique is capable of emulating the experimental datasets with less deviation, a fast convergence rate, and short execution time. � 2020 Elsevier Ltd Final 2023-05-29T08:10:38Z 2023-05-29T08:10:38Z 2020 Article 10.1016/j.energy.2020.116979 2-s2.0-85078659941 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078659941&doi=10.1016%2fj.energy.2020.116979&partnerID=40&md5=42326f66a948d0b45a39731193a4d6c0 https://irepository.uniten.edu.my/handle/123456789/25539 195 116979 Elsevier Ltd Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description Heuristic algorithms; Particle swarm optimization (PSO); Photovoltaic cells; Renewable energy resources; Solar power generation; Commercial applications; Environmental conditions; Fast convergence rate; Meta heuristic algorithm; Optimization algorithms; Parameters estimation; Particle swarm optimizers; PV models; Diodes; algorithm; alternative energy; electrode; model; optimization; parameter estimation; photovoltaic system; temperature effect
author2 56688582500
author_facet 56688582500
Yousri D.
Thanikanti S.B.
Allam D.
Ramachandaramurthy V.K.
Eteiba M.B.
format Article
author Yousri D.
Thanikanti S.B.
Allam D.
Ramachandaramurthy V.K.
Eteiba M.B.
spellingShingle Yousri D.
Thanikanti S.B.
Allam D.
Ramachandaramurthy V.K.
Eteiba M.B.
Fractional chaotic ensemble particle swarm optimizer for identifying the single, double, and three diode photovoltaic models� parameters
author_sort Yousri D.
title Fractional chaotic ensemble particle swarm optimizer for identifying the single, double, and three diode photovoltaic models� parameters
title_short Fractional chaotic ensemble particle swarm optimizer for identifying the single, double, and three diode photovoltaic models� parameters
title_full Fractional chaotic ensemble particle swarm optimizer for identifying the single, double, and three diode photovoltaic models� parameters
title_fullStr Fractional chaotic ensemble particle swarm optimizer for identifying the single, double, and three diode photovoltaic models� parameters
title_full_unstemmed Fractional chaotic ensemble particle swarm optimizer for identifying the single, double, and three diode photovoltaic models� parameters
title_sort fractional chaotic ensemble particle swarm optimizer for identifying the single, double, and three diode photovoltaic models� parameters
publisher Elsevier Ltd
publishDate 2023
_version_ 1806426176885882880
score 13.222552

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