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A comparative study of evolutionary algorithms and adapting control parameters for estimating the parameters of a single-diode photovoltaic module's model

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dc.citedby51
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.accessioned2023-05-29T06:11:29Z
dc.date.available2023-05-29T06:11:29Z
dc.date.issued2016
dc.descriptionAlgorithms; Diodes; Errors; Iterative methods; Least squares approximations; Mean square error; Optimization; Parameter estimation; Parameter extraction; Photovoltaic cells; Coefficient of determination; Differential Evolution; Electromagnetism-like algorithm; Hybrid evolutionary algorithm; Photovoltaic; Photovoltaic modules; Root mean square errors; Single-diode models; Evolutionary algorithms; algorithm; comparative study; electromagnetic method; estimation method; experimental design; numerical method; parameterization; performance assessment; photovoltaic systemen_US
dc.description.abstractThis paper proposes different evolutionary algorithms, such as differential evolution and electromagnetism-like algorithms, to extract the five parameters of a single-diode photovoltaic module's model. Hybrid evolutionary algorithms are proposed with integrated and adaptive mutation per iteration schemes. In addition, a new formula to adjust the mutation scaling factor and crossover rate for each generation is proposed. Analyses are performed based on experimental data points under different weather conditions to explain the robustness and reliability of the proposed methods. Results show that the proposed hybrid algorithms, namely, evolutionary algorithm with integrated mutation per iteration and evolutionary algorithm with adaptive mutation per iteration, exhibit better performance than electromagnetism-like algorithm and other methods in terms of accuracy, CPU execution time, and convergence. The proposed hybrid algorithms offer a root mean square error, mean bias error, coefficient of determination and CPU execution time around 0.062, 0.006 and 0.992, and less than 20 s respectively. Furthermore, the feasibility of the proposed methods is validated by comparing the obtained results with those of other methods under various statistical errors. As a conclusion, the proposed hybrid algorithms offer root mean square error and mean bias error less than other methods by 14% at least. � 2016 Elsevier Ltd.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1016/j.renene.2016.04.072
dc.identifier.epage389
dc.identifier.scopus2-s2.0-84964949903
dc.identifier.spage377
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84964949903&doi=10.1016%2fj.renene.2016.04.072&partnerID=40&md5=3e851b14217f91267a191eefaea19a76
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/22652
dc.identifier.volume96
dc.publisherElsevier Ltden_US
dc.sourceScopus
dc.sourcetitleRenewable Energy
dc.titleA comparative study of evolutionary algorithms and adapting control parameters for estimating the parameters of a single-diode photovoltaic module's modelen_US
dc.typeArticleen_US
dspace.entity.typePublication
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