Evaluation and identification of lightning models by artificial neural networks
Autor(a) principal: | |
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Data de Publicação: | 1999 |
Outros Autores: | , |
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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Repositório Institucional da UNESP |
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2946 |
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/openAccess2024-06-28T13:34:42Zoai:repositorio.unesp.br:11449/65954Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T20:37:01.866752Repositório 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_ |
1808129228348063744 |