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

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dc.citedby92
dc.contributor.authorYousri D.en_US
dc.contributor.authorThanikanti S.B.en_US
dc.contributor.authorAllam D.en_US
dc.contributor.authorRamachandaramurthy V.K.en_US
dc.contributor.authorEteiba M.B.en_US
dc.contributor.authorid56688582500en_US
dc.contributor.authorid56267551500en_US
dc.contributor.authorid55940454800en_US
dc.contributor.authorid6602912020en_US
dc.contributor.authorid6603527538en_US
dc.date.accessioned2023-05-29T08:10:38Z
dc.date.available2023-05-29T08:10:38Z
dc.date.issued2020
dc.descriptionHeuristic 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 effecten_US
dc.description.abstractSolar 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 Ltden_US
dc.description.natureFinalen_US
dc.identifier.ArtNo116979
dc.identifier.doi10.1016/j.energy.2020.116979
dc.identifier.scopus2-s2.0-85078659941
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85078659941&doi=10.1016%2fj.energy.2020.116979&partnerID=40&md5=42326f66a948d0b45a39731193a4d6c0
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25539
dc.identifier.volume195
dc.publisherElsevier Ltden_US
dc.sourceScopus
dc.sourcetitleEnergy
dc.titleFractional chaotic ensemble particle swarm optimizer for identifying the single, double, and three diode photovoltaic models� parametersen_US
dc.typeArticleen_US
dspace.entity.typePublication
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