Winding Shorts Detection in SCIG using Convolutional Neural Networks

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 i...

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Autor principal: Ferreira, Rhendson Alexandre
Outros Autores: Silveira, Luiz
Formato: bachelorThesis
Idioma:English
Publicado em: Universidade Federal do Rio Grande do Norte
Assuntos:
SCIG
Fault Detection
Convolutional Neural Networks
Renewable Energy
Endereço do item:https://repositorio.ufrn.br/handle/123456789/56805
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spelling ri-123456789-568052023-12-22T13:00:11Z Winding Shorts Detection in SCIG using Convolutional Neural Networks Winding Shorts Detection in SCIG using Convolutional Neural Networks Ferreira, Rhendson Alexandre 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 SCIG Fault Detection Convolutional Neural Networks Renewable Energy 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. 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. 2023-12-22T13:00:11Z 2023-12-22T13:00:11Z 2023-12-06 bachelorThesis 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 en Attribution-NonCommercial-NoDerivs 3.0 Brazil http://creativecommons.org/licenses/by-nc-nd/3.0/br/ application/pdf application/pdf Universidade Federal do Rio Grande do Norte Brasil UFRN Engenharia Elétrica Centro de Tecnologia
institution Repositório Institucional
collection RI - UFRN
language English
topic SCIG
Fault Detection
Convolutional Neural Networks
Renewable Energy
spellingShingle SCIG
Fault Detection
Convolutional Neural Networks
Renewable Energy
Ferreira, Rhendson Alexandre
Winding Shorts Detection in SCIG using Convolutional Neural Networks
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.
author2 Silveira, Luiz
author_facet Silveira, Luiz
Ferreira, Rhendson Alexandre
format bachelorThesis
author Ferreira, Rhendson Alexandre
author_sort Ferreira, Rhendson Alexandre
title Winding Shorts Detection in SCIG using Convolutional Neural Networks
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
publisher Universidade Federal do Rio Grande do Norte
publishDate 2023
url https://repositorio.ufrn.br/handle/123456789/56805
work_keys_str_mv AT ferreirarhendsonalexandre windingshortsdetectioninscigusingconvolutionalneuralnetworks
_version_ 1790056139146133504

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