Artificial neural networks for load flow and external equivalents studies

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dc.contributor.authorMueller, Heloisa H.
dc.contributor.authorRider, Marcos J. [UNESP]
dc.contributor.authorCastro, Carlos A.
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.contributor.institutionUniversidade Estadual de Campinas (UNICAMP)
dc.date.accessioned2014-05-20T13:29:16Z
dc.date.available2014-05-20T13:29:16Z
dc.date.issued2010-09-01
dc.description.abstractIn this paper an artificial neural network (ANN) based methodology is proposed for (a) solving the basic load flow, (b) solving the load flow considering the reactive power limits of generation (PV) buses, (c) determining a good quality load flow starting point for ill-conditioned systems, and (d) computing static external equivalent circuits. An analysis of the input data required as well as the ANN architecture is presented. A multilayer perceptron trained with the Levenberg-Marquardt second order method is used. The proposed methodology was tested with the IEEE 30- and 57-bus, and an ill-conditioned 11-bus system. Normal operating conditions (base case) and several contingency situations including different load and generation scenarios have been considered. Simulation results show the excellent performance of the ANN for solving problems (a)-(d). (C) 2010 Elsevier B.V. All rights reserved.en
dc.description.affiliationUniv Estadual Paulista, DEE FEIS UNESP, BR-15385000 Ilha Solteira, SP, Brazil
dc.description.affiliationUniv Estadual Campinas, DSEE FEEC UNICAMP, BR-13083852 Campinas, SP, Brazil
dc.description.affiliationUnespUniv Estadual Paulista, DEE FEIS UNESP, BR-15385000 Ilha Solteira, SP, Brazil
dc.format.extent1033-1041
dc.identifierhttp://dx.doi.org/10.1016/j.epsr.2010.01.008
dc.identifier.citationElectric Power Systems Research. Lausanne: Elsevier B.V. Sa, v. 80, n. 9, p. 1033-1041, 2010.
dc.identifier.doi10.1016/j.epsr.2010.01.008
dc.identifier.issn0378-7796
dc.identifier.urihttp://hdl.handle.net/11449/9864
dc.identifier.wosWOS:000279293300005
dc.language.isoeng
dc.publisherElsevier B.V. Sa
dc.relation.ispartofElectric Power Systems Research
dc.relation.ispartofjcr2.856
dc.relation.ispartofsjr1,048
dc.rights.accessRightsAcesso restrito
dc.sourceWeb of Science
dc.subjectArtificial neural networksen
dc.subjectLoad flowen
dc.subjectReactive power limits of generation busesen
dc.subjectLoad flow with step size optimizationen
dc.subjectStatic external equivalentsen
dc.titleArtificial neural networks for load flow and external equivalents studiesen
dc.typeArtigo
dcterms.licensehttp://www.elsevier.com/about/open-access/open-access-policies/article-posting-policy
dcterms.rightsHolderElsevier B.V. Sa
unesp.campusUniversidade Estadual Paulista (Unesp), Faculdade de Engenharia, Ilha Solteirapt
unesp.departmentEngenharia Elétrica - FEISpt

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Ilha Solteira - FEIS - Faculdade de Engenharia