dc.contributorUniversidade Estadual Paulista (Unesp)
dc.date.accessioned2014-05-20T13:29:07Z
dc.date.accessioned2022-10-05T13:27:46Z
dc.date.available2014-05-20T13:29:07Z
dc.date.available2022-10-05T13:27:46Z
dc.date.created2014-05-20T13:29:07Z
dc.date.issued2011-01-01
dc.identifierApplied Soft Computing. Amsterdam: Elsevier B.V., v. 11, n. 1, p. 706-715, 2011.
dc.identifier1568-4946
dc.identifierhttp://hdl.handle.net/11449/9775
dc.identifier10.1016/j.asoc.2009.12.032
dc.identifierWOS:000281591300070
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/3886060
dc.description.abstractThis work presents a methodology to analyze electric power systems transient stability for first swing using a neural network based on adaptive resonance theory (ART) architecture, called Euclidean ARTMAP neural network. The ART architectures present plasticity and stability characteristics, which are very important for the training and to execute the analysis in a fast way. The Euclidean ARTMAP version provides more accurate and faster solutions, when compared to the fuzzy ARTMAP configuration. Three steps are necessary for the network working, training, analysis and continuous training. The training step requires much effort (processing) while the analysis is effectuated almost without computational effort. The proposed network allows approaching several topologies of the electric system at the same time; therefore it is an alternative for real time transient stability of electric power systems. To illustrate the proposed neural network an application is presented for a multi-machine electric power systems composed of 10 synchronous machines, 45 buses and 73 transmission lines. (C) 2010 Elsevier B.V. All rights reserved.
dc.languageeng
dc.publisherElsevier B.V.
dc.relationApplied Soft Computing
dc.relation3.907
dc.relation1,199
dc.rightsAcesso restrito
dc.sourceWeb of Science
dc.subjectElectric power systems
dc.subjectTransient stability analysis
dc.subjectNeural network
dc.subjectEuclidean ARTMAP neural network
dc.titleNeural network based on adaptive resonance theory with continuous training for multi-configuration transient stability analysis of electric power systems
dc.typeArtigo


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