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dc.contributor.authorLotufo, Anna Diva P.-
dc.contributor.authorLopes, Mara Lucia M.-
dc.contributor.authorMinussi, Carlos R.-
dc.date.accessioned2014-05-20T13:28:59Z-
dc.date.accessioned2016-10-25T16:48:28Z-
dc.date.available2014-05-20T13:28:59Z-
dc.date.available2016-10-25T16:48:28Z-
dc.date.issued2007-05-01-
dc.identifierhttp://dx.doi.org/10.1016/j.epsr.2005.09.020-
dc.identifier.citationElectric Power Systems Research. Lausanne: Elsevier B.V. Sa, v. 77, n. 7, p. 730-738, 2007.-
dc.identifier.issn0378-7796-
dc.identifier.urihttp://hdl.handle.net/11449/9706-
dc.identifier.urihttp://acervodigital.unesp.br/handle/11449/9706-
dc.description.abstractThis work presents a procedure for transient stability analysis and preventive control of electric power systems, which is formulated by a multilayer feedforward neural network. The neural network training is realized by using the back-propagation algorithm with fuzzy controller and adaptation of the inclination and translation parameters of the nonlinear function. These procedures provide a faster convergence and more precise results, if compared to the traditional back-propagation algorithm. The adaptation of the training rate is effectuated by using the information of the global error and global error variation. After finishing the training, the neural network is capable of estimating the security margin and the sensitivity analysis. Considering this information, it is possible to develop a method for the realization of the security correction (preventive control) for levels considered appropriate to the system, based on generation reallocation and load shedding. An application for a multimachine power system is presented to illustrate the proposed methodology. (c) 2006 Elsevier B.V. All rights reserved.en
dc.format.extent730-738-
dc.language.isoeng-
dc.publisherElsevier B.V.-
dc.sourceWeb of Science-
dc.subjectsensitivity analysispt
dc.subjectpreventive controlpt
dc.subjecttransient stabilitypt
dc.subjectneural networkspt
dc.subjectback-propagationpt
dc.titleSensitivity analysis by neural networks applied to power systems transient stabilityen
dc.typeoutro-
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)-
dc.description.affiliationUNESP, Dept Elect Engn, BR-15385000 Ilha Solteira, SP, Brazil-
dc.description.affiliationUnespUNESP, Dept Elect Engn, BR-15385000 Ilha Solteira, SP, Brazil-
dc.identifier.doi10.1016/j.epsr.2005.09.020-
dc.identifier.wosWOS:000246018700002-
dc.rights.accessRightsAcesso restrito-
dc.relation.ispartofElectric Power Systems Research-
Appears in Collections:Artigos, TCCs, Teses e Dissertações da Unesp

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