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Parameters extraction of double diode photovoltaic module's model based on hybrid evolutionary algorithm

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dc.citedby121
dc.contributor.authorMuhsen D.H.en_US
dc.contributor.authorGhazali A.B.en_US
dc.contributor.authorKhatib T.en_US
dc.contributor.authorAbed I.A.en_US
dc.contributor.authorid56728928200en_US
dc.contributor.authorid56727852400en_US
dc.contributor.authorid31767521400en_US
dc.contributor.authorid55568292900en_US
dc.date.accessioned2023-05-29T06:00:01Z
dc.date.available2023-05-29T06:00:01Z
dc.date.issued2015
dc.descriptionAlgorithms; Diodes; Errors; Extraction; Iterative methods; Mean square error; Optimization; Parameter estimation; Parameter extraction; Photovoltaic cells; Differential evolution algorithms; Diode modeling; Electromagnetism-like algorithm; Fast convergence speed; Hybrid evolutionary algorithm; IV characteristics; Photovoltaic model; Root mean square errors; Evolutionary algorithmsen_US
dc.description.abstractAccurate modeling of photovoltaic (PV) modules is helpful in designing and assessing the energy production of PV systems. A new version of the differential evolution (DE) algorithm, called differential evolution with integrated mutation per iteration (DEIM), is proposed in this study to extract the seven parameters of a double-diode PV module model. This algorithm applies the attraction-repulsion concept of an electromagnetism-like algorithm to boost the mutation operation of the conventional DE algorithm. Moreover, a new adaptive strategy is proposed to tune mutation scaling and crossover rate for each generation. The proposed model is validated through experimental data and other models, which have been proposed in literature using various statistical errors. Results show that DEIM exhibits high accuracy and fast convergence speed compared with other methods. The average root mean square error, mean bias error, and absolute error at maximum power point of the proposed model are 1.713%, 0.149%, and 4.515%, respectively. � 2015 Elsevier Ltd. All rights reserved.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1016/j.enconman.2015.08.023
dc.identifier.epage561
dc.identifier.scopus2-s2.0-84939782602
dc.identifier.spage552
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84939782602&doi=10.1016%2fj.enconman.2015.08.023&partnerID=40&md5=75392c2df20fd6e8b46d34fb25edbe73
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/22289
dc.identifier.volume105
dc.publisherElsevier Ltden_US
dc.sourceScopus
dc.sourcetitleEnergy Conversion and Management
dc.titleParameters extraction of double diode photovoltaic module's model based on hybrid evolutionary algorithmen_US
dc.typeArticleen_US
dspace.entity.typePublication
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