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A new high performance method for determining the parameters of PV cells and modules based on guaranteed convergence particle swarm optimization

dc.contributor.authorNunes, H.G.G.
dc.contributor.authorPombo, José Álvaro Nunes
dc.contributor.authorMariano, S.
dc.contributor.authorCalado, M. do Rosário
dc.contributor.authorFelippe de Souza, J.A.M.
dc.date.accessioned2019-05-02T13:13:25Z
dc.date.available2019-05-02T13:13:25Z
dc.date.issued2018-02
dc.description.abstractDetermining the mathematical model parameters of photovoltaic (PV) cells and modules represents a great challenge. In the last few years, several analytical, numerical and hybrid methods have been proposed for extracting the PV model parameters from datasheets provided by the manufacturers or from experimental data, although it is difficult to determine highly reliable solutions quickly and accurately. In this paper, we propose a new method for determining the PV parameters of both the single-diode and the double-diode models, based on the guaranteed convergence particle swarm optimization (GCPSO), using experimental data under different operating conditions. The main advantage of this method is its ability to avoid premature convergence in the optimization of complex and multimodal objective functions, such as the function that determines PV parameters. To validate performance, the GCPSO method was compared with several analytical, numerical and hybrid methods found in the literature. This validation considered three different case studies. The first two are important reference case studies in the literature and have been widely used by researchers. The third was performed in an experimental environment, in order to test the proposed method under a real implementation. The proposed methodology can find highly accurate solutions while demanding a reduced computational cost. Comparisons with other published methods demonstrate that the proposed method produces very good results in the extraction of the PV model parameters.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1016/j.apenergy.2017.11.078pt_PT
dc.identifier.issn03062619
dc.identifier.urihttp://hdl.handle.net/10400.6/7056
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.subjectDouble-diode modelpt_PT
dc.subjectExperimental datapt_PT
dc.subjectGuaranteed convergence particle swarm optimizationpt_PT
dc.subjectParameter extractionpt_PT
dc.subjectSingle-diode modelpt_PT
dc.titleA new high performance method for determining the parameters of PV cells and modules based on guaranteed convergence particle swarm optimizationpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage791pt_PT
oaire.citation.startPage774pt_PT
oaire.citation.titleApplied Energypt_PT
oaire.citation.volume211pt_PT
person.familyNameGarcia Nunes
person.familyNamePombo
person.familyNamePinto Simões Mariano
person.familyNameCalado
person.givenNameHugo Gabriel
person.givenNameJose
person.givenNameSílvio José
person.givenNameM. do Rosário
person.identifier.ciencia-id7615-8E00-8084
person.identifier.ciencia-id541F-E2B4-D66D
person.identifier.ciencia-id9115-032B-370B
person.identifier.orcid0000-0002-6029-7032
person.identifier.orcid0000-0002-8727-0067
person.identifier.orcid0000-0002-6102-5872
person.identifier.orcid0000-0002-5206-487X
person.identifier.ridV-4684-2018
person.identifier.ridN-6834-2013
person.identifier.ridN-6809-2013
person.identifier.scopus-author-id57195107686
person.identifier.scopus-author-id34977533800
person.identifier.scopus-author-id35612517200
person.identifier.scopus-author-id9338016700
rcaap.embargofctCopyright cedido à editora no momento da publicaçãopt_PT
rcaap.rightsclosedAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication40ed2120-90cf-41fa-84fc-d1c85cf8d848
relation.isAuthorOfPublicationcce2060a-24b8-441b-8896-cb4d0b3d3e83
relation.isAuthorOfPublicationcdbb9afc-4123-45ca-a946-89bafda7ab68
relation.isAuthorOfPublication321aefdd-cd1f-4dd6-878e-c904b3ef89ab
relation.isAuthorOfPublication.latestForDiscovery321aefdd-cd1f-4dd6-878e-c904b3ef89ab

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