Winding Shorts Detection in SCIG using Convolutional Neural Networks

Bibliographic Details
Main Author: Ferreira, Rhendson Alexandre
Publication Date: 2023
Format: Bachelor thesis
Language: eng
Source: Repositório Institucional da UFRN
dARK ID: ark:/41046/001300001nqr3
Download full: https://repositorio.ufrn.br/handle/123456789/56805
Summary: In the rapidly evolving landscape of renewable energy, unprecedented growth is occurring in wind power generation. At the core of this growth are Squirrel Cage Induction Generators (SCIGs), known for their simplicity, robustness, and cost-effectiveness. However, the reliability of these generators is often compromised by winding short faults, specifically in the stator winding. This paper presents a novel approach for early detection of these faults using Convolutional Neural Networks (CNNs), thereby enhancing the reliability and reducing the maintenance costs of wind power generation systems.
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spelling Winding Shorts Detection in SCIG using Convolutional Neural NetworksWinding Shorts Detection in SCIG using Convolutional Neural NetworksSCIGFault DetectionConvolutional Neural NetworksRenewable EnergyIn the rapidly evolving landscape of renewable energy, unprecedented growth is occurring in wind power generation. At the core of this growth are Squirrel Cage Induction Generators (SCIGs), known for their simplicity, robustness, and cost-effectiveness. However, the reliability of these generators is often compromised by winding short faults, specifically in the stator winding. This paper presents a novel approach for early detection of these faults using Convolutional Neural Networks (CNNs), thereby enhancing the reliability and reducing the maintenance costs of wind power generation systems.In the rapidly evolving landscape of renewable energy, unprecedented growth is occurring in wind power generation. At the core of this growth are Squirrel Cage Induction Generators (SCIGs), known for their simplicity, robustness, and cost-effectiveness. However, the reliability of these generators is often compromised by winding short faults, specifically in the stator winding. This paper presents a novel approach for early detection of these faults using Convolutional Neural Networks (CNNs), thereby enhancing the reliability and reducing the maintenance costs of wind power generation systems.Universidade Federal do Rio Grande do NorteBrasilUFRNEngenharia ElétricaCentro de TecnologiaSilveira, Luizhttps://orcid.org/0000-0001-6167-1893http://lattes.cnpq.br/5714183212530259Barros, Lucianohttp://lattes.cnpq.br/5175817442792763Lins, Hertzhttp://lattes.cnpq.br/4139452169580807Ferreira, Rhendson Alexandre2023-12-22T13:00:11Z2023-12-22T13:00:11Z2023-12-06info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/bachelorThesisapplication/pdfapplication/pdfFERREIRA, Rhendson Alexandre. Winding Shorts Detection in SCIG using Convolutional Neural Networks. 2023. 6 f. Trabalho de Conclusão de Curso (Graduação em Engenharia Elétrica) - Departamento de Engenharia Elétrica, Universidade Federal do Rio Grande do Norte, 2023.https://repositorio.ufrn.br/handle/123456789/56805ark:/41046/001300001nqr3Attribution-NonCommercial-NoDerivs 3.0 Brazilhttp://creativecommons.org/licenses/by-nc-nd/3.0/br/info:eu-repo/semantics/openAccessengreponame:Repositório Institucional da UFRNinstname:Universidade Federal do Rio Grande do Norte (UFRN)instacron:UFRN2023-12-22T13:00:11Zoai:repositorio.ufrn.br:123456789/56805Repositório InstitucionalPUBhttp://repositorio.ufrn.br/oai/repositorio@bczm.ufrn.bropendoar:2023-12-22T13:00:11Repositório Institucional da UFRN - Universidade Federal do Rio Grande do Norte (UFRN)false
dc.title.none.fl_str_mv Winding Shorts Detection in SCIG using Convolutional Neural Networks
Winding Shorts Detection in SCIG using Convolutional Neural Networks
title Winding Shorts Detection in SCIG using Convolutional Neural Networks
spellingShingle Winding Shorts Detection in SCIG using Convolutional Neural Networks
Ferreira, Rhendson Alexandre
SCIG
Fault Detection
Convolutional Neural Networks
Renewable Energy
title_short Winding Shorts Detection in SCIG using Convolutional Neural Networks
title_full Winding Shorts Detection in SCIG using Convolutional Neural Networks
title_fullStr Winding Shorts Detection in SCIG using Convolutional Neural Networks
title_full_unstemmed Winding Shorts Detection in SCIG using Convolutional Neural Networks
title_sort Winding Shorts Detection in SCIG using Convolutional Neural Networks
author Ferreira, Rhendson Alexandre
author_facet Ferreira, Rhendson Alexandre
author_role author
dc.contributor.none.fl_str_mv Silveira, Luiz
https://orcid.org/0000-0001-6167-1893
http://lattes.cnpq.br/5714183212530259
Barros, Luciano
http://lattes.cnpq.br/5175817442792763
Lins, Hertz
http://lattes.cnpq.br/4139452169580807
dc.contributor.author.fl_str_mv Ferreira, Rhendson Alexandre
dc.subject.por.fl_str_mv SCIG
Fault Detection
Convolutional Neural Networks
Renewable Energy
topic SCIG
Fault Detection
Convolutional Neural Networks
Renewable Energy
description In the rapidly evolving landscape of renewable energy, unprecedented growth is occurring in wind power generation. At the core of this growth are Squirrel Cage Induction Generators (SCIGs), known for their simplicity, robustness, and cost-effectiveness. However, the reliability of these generators is often compromised by winding short faults, specifically in the stator winding. This paper presents a novel approach for early detection of these faults using Convolutional Neural Networks (CNNs), thereby enhancing the reliability and reducing the maintenance costs of wind power generation systems.
publishDate 2023
dc.date.none.fl_str_mv 2023-12-22T13:00:11Z
2023-12-22T13:00:11Z
2023-12-06
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/bachelorThesis
format bachelorThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv FERREIRA, Rhendson Alexandre. Winding Shorts Detection in SCIG using Convolutional Neural Networks. 2023. 6 f. Trabalho de Conclusão de Curso (Graduação em Engenharia Elétrica) - Departamento de Engenharia Elétrica, Universidade Federal do Rio Grande do Norte, 2023.
https://repositorio.ufrn.br/handle/123456789/56805
dc.identifier.dark.fl_str_mv ark:/41046/001300001nqr3
identifier_str_mv FERREIRA, Rhendson Alexandre. Winding Shorts Detection in SCIG using Convolutional Neural Networks. 2023. 6 f. Trabalho de Conclusão de Curso (Graduação em Engenharia Elétrica) - Departamento de Engenharia Elétrica, Universidade Federal do Rio Grande do Norte, 2023.
ark:/41046/001300001nqr3
url https://repositorio.ufrn.br/handle/123456789/56805
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv Attribution-NonCommercial-NoDerivs 3.0 Brazil
http://creativecommons.org/licenses/by-nc-nd/3.0/br/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Attribution-NonCommercial-NoDerivs 3.0 Brazil
http://creativecommons.org/licenses/by-nc-nd/3.0/br/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Universidade Federal do Rio Grande do Norte
Brasil
UFRN
Engenharia Elétrica
Centro de Tecnologia
publisher.none.fl_str_mv Universidade Federal do Rio Grande do Norte
Brasil
UFRN
Engenharia Elétrica
Centro de Tecnologia
dc.source.none.fl_str_mv reponame:Repositório Institucional da UFRN
instname:Universidade Federal do Rio Grande do Norte (UFRN)
instacron:UFRN
instname_str Universidade Federal do Rio Grande do Norte (UFRN)
instacron_str UFRN
institution UFRN
reponame_str Repositório Institucional da UFRN
collection Repositório Institucional da UFRN
repository.name.fl_str_mv Repositório Institucional da UFRN - Universidade Federal do Rio Grande do Norte (UFRN)
repository.mail.fl_str_mv repositorio@bczm.ufrn.br
_version_ 1839178897516658688

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