Evaluation and identification of lightning models by artificial neural networks

Detalhes bibliográficos
Autor(a) principal: da Silva, Ivan Nunes [UNESP]
Data de Publicação: 1999
Outros Autores: de Souza, Andre Nunes [UNESP], Bordon, Mario Eduardo [UNESP]
Tipo de documento: Artigo de conferência
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1109/IJCNN.1999.830762
http://hdl.handle.net/11449/65954
Resumo: This 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.
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spelling Evaluation and identification of lightning models by artificial neural networksAtmospheric humidityComputer simulationElectric fieldsElectric potentialLightningMathematical modelsPressure effectsThermal effectsWaveform analysisCritical disruptive voltageElectrical field intensityNeural networksThis 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.State Univ of Sao Paulo - UNESP, Sao PauloState Univ of Sao Paulo - UNESP, Sao PauloUniversidade Estadual Paulista (Unesp)da Silva, Ivan Nunes [UNESP]de Souza, Andre Nunes [UNESP]Bordon, Mario Eduardo [UNESP]2014-05-27T11:19:49Z2014-05-27T11:19:49Z1999-12-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject3816-3820http://dx.doi.org/10.1109/IJCNN.1999.830762Proceedings of the International Joint Conference on Neural Networks, v. 6, p. 3816-3820.http://hdl.handle.net/11449/6595410.1109/IJCNN.1999.8307622-s2.0-003333348082127759604946865589838844298232Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengProceedings of the International Joint Conference on Neural Networksinfo:eu-repo/semantics/openAccess2021-10-22T20:56:23Zoai:repositorio.unesp.br:11449/65954Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462021-10-22T20:56:23Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Evaluation and identification of lightning models by artificial neural networks
title Evaluation and identification of lightning models by artificial neural networks
spellingShingle Evaluation and identification of lightning models by artificial neural networks
da Silva, Ivan Nunes [UNESP]
Atmospheric humidity
Computer simulation
Electric fields
Electric potential
Lightning
Mathematical models
Pressure effects
Thermal effects
Waveform analysis
Critical disruptive voltage
Electrical field intensity
Neural networks
title_short Evaluation and identification of lightning models by artificial neural networks
title_full Evaluation and identification of lightning models by artificial neural networks
title_fullStr Evaluation and identification of lightning models by artificial neural networks
title_full_unstemmed Evaluation and identification of lightning models by artificial neural networks
title_sort Evaluation and identification of lightning models by artificial neural networks
author da Silva, Ivan Nunes [UNESP]
author_facet da Silva, Ivan Nunes [UNESP]
de Souza, Andre Nunes [UNESP]
Bordon, Mario Eduardo [UNESP]
author_role author
author2 de Souza, Andre Nunes [UNESP]
Bordon, Mario Eduardo [UNESP]
author2_role author
author
dc.contributor.none.fl_str_mv Universidade Estadual Paulista (Unesp)
dc.contributor.author.fl_str_mv da Silva, Ivan Nunes [UNESP]
de Souza, Andre Nunes [UNESP]
Bordon, Mario Eduardo [UNESP]
dc.subject.por.fl_str_mv Atmospheric humidity
Computer simulation
Electric fields
Electric potential
Lightning
Mathematical models
Pressure effects
Thermal effects
Waveform analysis
Critical disruptive voltage
Electrical field intensity
Neural networks
topic Atmospheric humidity
Computer simulation
Electric fields
Electric potential
Lightning
Mathematical models
Pressure effects
Thermal effects
Waveform analysis
Critical disruptive voltage
Electrical field intensity
Neural networks
description This 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.
publishDate 1999
dc.date.none.fl_str_mv 1999-12-01
2014-05-27T11:19:49Z
2014-05-27T11:19:49Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/conferenceObject
format conferenceObject
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://dx.doi.org/10.1109/IJCNN.1999.830762
Proceedings of the International Joint Conference on Neural Networks, v. 6, p. 3816-3820.
http://hdl.handle.net/11449/65954
10.1109/IJCNN.1999.830762
2-s2.0-0033333480
8212775960494686
5589838844298232
url http://dx.doi.org/10.1109/IJCNN.1999.830762
http://hdl.handle.net/11449/65954
identifier_str_mv Proceedings of the International Joint Conference on Neural Networks, v. 6, p. 3816-3820.
10.1109/IJCNN.1999.830762
2-s2.0-0033333480
8212775960494686
5589838844298232
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Proceedings of the International Joint Conference on Neural Networks
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 3816-3820
dc.source.none.fl_str_mv Scopus
reponame:Repositório Institucional da UNESP
instname:Universidade Estadual Paulista (UNESP)
instacron:UNESP
instname_str Universidade Estadual Paulista (UNESP)
instacron_str UNESP
institution UNESP
reponame_str Repositório Institucional da UNESP
collection Repositório Institucional da UNESP
repository.name.fl_str_mv Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)
repository.mail.fl_str_mv
_version_ 1799965322888871936

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