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dc.contributor.authorda Silva, Ivan Nunes [UNESP]
dc.contributor.authorde Souza, Andre Nunes [UNESP]
dc.contributor.authorBordon, Mario Eduardo [UNESP]
dc.identifier.citationProceedings of the International Joint Conference on Neural Networks, v. 6, p. 3816-3820.
dc.description.abstractThis paper describes a novel approach for mapping lightning models using artificial neural networks. The networks acts as identifier of structural features of the lightning models so that output parameters can be estimated and generalized from an input parameter set. Simulation examples are presented to validate the proposed approach. More specifically, the neural networks are used to compute electrical field intensity and critical disruptive voltage taking into account several atmospheric and structural factors, such as pressure, temperature, humidity, distance between phases, height of bus bars, and wave forms. A comparative analysis with other approaches is also provided to illustrate this new methodology.en
dc.relation.ispartofProceedings of the International Joint Conference on Neural Networks
dc.subjectAtmospheric humidity
dc.subjectComputer simulation
dc.subjectElectric fields
dc.subjectElectric potential
dc.subjectMathematical models
dc.subjectPressure effects
dc.subjectThermal effects
dc.subjectWaveform analysis
dc.subjectCritical disruptive voltage
dc.subjectElectrical field intensity
dc.subjectNeural networks
dc.titleEvaluation and identification of lightning models by artificial neural networksen
dc.typeTrabalho apresentado em evento
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.description.affiliationState Univ of Sao Paulo - UNESP, Sao Paulo
dc.description.affiliationUnespState Univ of Sao Paulo - UNESP, Sao Paulo
dc.rights.accessRightsAcesso aberto
unesp.campusUniversidade Estadual Paulista (Unesp), Faculdade de Engenharia, Baurupt
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