Application of Artificial Neural Network for Analysis of Self-Excited Induction Generator

Autores
Khela, Raja Singh; Bansal, Raj Kumar; Sandhu, K. S.; Goel, Ashok Kumar
Año de publicación
2006
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
It is observed that conventional techniques to analyse the steady state analysis of Self-Excited Induction Generator (SEIG) involve cumbersome mathematical procedures. In this paper an Artificial Intelligence (AI) technique has been used to analyse the behaviour of Self-Excited Induction Generator, which does not require rigorous modelling as required in conventional techniques. Proposed Artificial Neural Network (ANN) model has been implemented to predict the effect of speed, capacitance and load on generated voltage and frequency of SEIG. Experimental data is used for the training of ANN. Results obtained from the trained ANN are found to be in close agreement with the experimental results.
Facultad de Informática
Materia
Ciencias Informáticas
Connectionism and neural nets
ARTIFICIAL INTELLIGENCE
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc/3.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/9541
Acceder
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oai_identifier_str oai:sedici.unlp.edu.ar:10915/9541
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repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling Application of Artificial Neural Network for Analysis of Self-Excited Induction GeneratorKhela, Raja SinghBansal, Raj KumarSandhu, K. S.Goel, Ashok KumarCiencias InformáticasConnectionism and neural netsARTIFICIAL INTELLIGENCEIt is observed that conventional techniques to analyse the steady state analysis of Self-Excited Induction Generator (SEIG) involve cumbersome mathematical procedures. In this paper an Artificial Intelligence (AI) technique has been used to analyse the behaviour of Self-Excited Induction Generator, which does not require rigorous modelling as required in conventional techniques. Proposed Artificial Neural Network (ANN) model has been implemented to predict the effect of speed, capacitance and load on generated voltage and frequency of SEIG. Experimental data is used for the training of ANN. Results obtained from the trained ANN are found to be in close agreement with the experimental results.Facultad de Informática2006-10info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArticulohttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdf73-79http://sedici.unlp.edu.ar/handle/10915/9541enginfo:eu-repo/semantics/altIdentifier/url/http://journal.info.unlp.edu.ar/wp-content/uploads/JCST-Oct06-3.pdfinfo:eu-repo/semantics/altIdentifier/issn/1666-6038info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc/3.0/Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T10:50:44Zoai:sedici.unlp.edu.ar:10915/9541Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 10:50:44.334SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Application of Artificial Neural Network for Analysis of Self-Excited Induction Generator
title Application of Artificial Neural Network for Analysis of Self-Excited Induction Generator
spellingShingle Application of Artificial Neural Network for Analysis of Self-Excited Induction Generator
Khela, Raja Singh
Ciencias Informáticas
Connectionism and neural nets
ARTIFICIAL INTELLIGENCE
title_short Application of Artificial Neural Network for Analysis of Self-Excited Induction Generator
title_full Application of Artificial Neural Network for Analysis of Self-Excited Induction Generator
title_fullStr Application of Artificial Neural Network for Analysis of Self-Excited Induction Generator
title_full_unstemmed Application of Artificial Neural Network for Analysis of Self-Excited Induction Generator
title_sort Application of Artificial Neural Network for Analysis of Self-Excited Induction Generator
dc.creator.none.fl_str_mv Khela, Raja Singh
Bansal, Raj Kumar
Sandhu, K. S.
Goel, Ashok Kumar
author Khela, Raja Singh
author_facet Khela, Raja Singh
Bansal, Raj Kumar
Sandhu, K. S.
Goel, Ashok Kumar
author_role author
author2 Bansal, Raj Kumar
Sandhu, K. S.
Goel, Ashok Kumar
author2_role author
author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
Connectionism and neural nets
ARTIFICIAL INTELLIGENCE
topic Ciencias Informáticas
Connectionism and neural nets
ARTIFICIAL INTELLIGENCE
dc.description.none.fl_txt_mv It is observed that conventional techniques to analyse the steady state analysis of Self-Excited Induction Generator (SEIG) involve cumbersome mathematical procedures. In this paper an Artificial Intelligence (AI) technique has been used to analyse the behaviour of Self-Excited Induction Generator, which does not require rigorous modelling as required in conventional techniques. Proposed Artificial Neural Network (ANN) model has been implemented to predict the effect of speed, capacitance and load on generated voltage and frequency of SEIG. Experimental data is used for the training of ANN. Results obtained from the trained ANN are found to be in close agreement with the experimental results.
Facultad de Informática
description It is observed that conventional techniques to analyse the steady state analysis of Self-Excited Induction Generator (SEIG) involve cumbersome mathematical procedures. In this paper an Artificial Intelligence (AI) technique has been used to analyse the behaviour of Self-Excited Induction Generator, which does not require rigorous modelling as required in conventional techniques. Proposed Artificial Neural Network (ANN) model has been implemented to predict the effect of speed, capacitance and load on generated voltage and frequency of SEIG. Experimental data is used for the training of ANN. Results obtained from the trained ANN are found to be in close agreement with the experimental results.
publishDate 2006
dc.date.none.fl_str_mv 2006-10
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Articulo
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/9541
url http://sedici.unlp.edu.ar/handle/10915/9541
dc.language.none.fl_str_mv eng
language eng
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info:eu-repo/semantics/altIdentifier/issn/1666-6038
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc/3.0/
Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc/3.0/
Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0)
dc.format.none.fl_str_mv application/pdf
73-79
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
instacron:UNLP
reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
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