Type-2 Fuzzy Control of DFIG for Wind Energy Conversion Systems

Detalhes bibliográficos
Autor(a) principal: Kouadria, Mohamed Abdeldjabbar
Data de Publicação: 2025
Outros Autores: Kouadria, Selman, Bouzid, Mohamed Amine
Tipo de documento: Artigo
Idioma: eng
Título da fonte: ITEGAM-JETIA
Texto Completo: https://itegam-jetia.org/journal/index.php/jetia/article/view/1640
Resumo: This study develops and designs a Type 2 fuzzy controller technique for application inwind turbines directly linked to the grid and incorporating variable-speed doubly fed inductiongenerators (DFIG).Type 2 fuzzy theory is proposed with the aim of enhancing system performance. Unlike Type 1 fuzzysystems, it accommodates a wide range of uncertainties and dynamic nonlinearities that mayconstrain the system's operational efficiency.Type 2 fuzzy logic provides an effective approach to managing linguistic uncertainty by modelingthe ambiguity and limited reliability of information, thereby reducing the overall level of uncertaintywithin the system.Both Type 1 Fuzzy Logic Control (T1FLC) and Type 2 Fuzzy Logic Control (T2FLC)techniques were employed in direct and indirect modes. The two control methods weredeveloped, their performances were evaluated, and the most effective control method interms of reference tracking and robustness was identified. This comparative analysis isderived from a series of tests performed under identical conditions during bothtransient and steady-state operations of the system.The simulation results demonstrate that the proposed method exhibits significant resilience toparameter variations and unstructured uncertainties.
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spelling Type-2 Fuzzy Control of DFIG for Wind Energy Conversion SystemsThis study develops and designs a Type 2 fuzzy controller technique for application inwind turbines directly linked to the grid and incorporating variable-speed doubly fed inductiongenerators (DFIG).Type 2 fuzzy theory is proposed with the aim of enhancing system performance. Unlike Type 1 fuzzysystems, it accommodates a wide range of uncertainties and dynamic nonlinearities that mayconstrain the system's operational efficiency.Type 2 fuzzy logic provides an effective approach to managing linguistic uncertainty by modelingthe ambiguity and limited reliability of information, thereby reducing the overall level of uncertaintywithin the system.Both Type 1 Fuzzy Logic Control (T1FLC) and Type 2 Fuzzy Logic Control (T2FLC)techniques were employed in direct and indirect modes. The two control methods weredeveloped, their performances were evaluated, and the most effective control method interms of reference tracking and robustness was identified. This comparative analysis isderived from a series of tests performed under identical conditions during bothtransient and steady-state operations of the system.The simulation results demonstrate that the proposed method exhibits significant resilience toparameter variations and unstructured uncertainties.ITEGAM - Instituto de Tecnologia e Educação Galileo da Amazônia2025-06-26info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionPeer-reviewed Articleapplication/pdfhttps://itegam-jetia.org/journal/index.php/jetia/article/view/164010.5935/jetia.v11i53.1640ITEGAM-JETIA; v.11 n.53 2025; 154-161ITEGAM-JETIA; v.11 n.53 2025; 154-161ITEGAM-JETIA; v.11 n.53 2025; 154-1612447-022810.5935/jetia.v11i53reponame:ITEGAM-JETIAinstname:Instituto de Tecnologia e Educação Galileo da Amazônia (ITEGAM)instacron:ITEGAMenghttps://itegam-jetia.org/journal/index.php/jetia/article/view/1640/1032Copyright (c) 2025 ITEGAM-JETIAinfo:eu-repo/semantics/openAccessKouadria, Mohamed AbdeldjabbarKouadria, SelmanBouzid, Mohamed Amine2025-06-30T15:01:12Zoai:ojs.itegam-jetia.org:article/1640Revistahttps://itegam-jetia.org/journal/index.php/jetiaPRIhttps://itegam-jetia.org/journal/index.php/jetia/oaieditor@itegam-jetia.orgopendoar:2025-06-30T15:01:12ITEGAM-JETIA - Instituto de Tecnologia e Educação Galileo da Amazônia (ITEGAM)false
dc.title.none.fl_str_mv Type-2 Fuzzy Control of DFIG for Wind Energy Conversion Systems
title Type-2 Fuzzy Control of DFIG for Wind Energy Conversion Systems
spellingShingle Type-2 Fuzzy Control of DFIG for Wind Energy Conversion Systems
Kouadria, Mohamed Abdeldjabbar
title_short Type-2 Fuzzy Control of DFIG for Wind Energy Conversion Systems
title_full Type-2 Fuzzy Control of DFIG for Wind Energy Conversion Systems
title_fullStr Type-2 Fuzzy Control of DFIG for Wind Energy Conversion Systems
title_full_unstemmed Type-2 Fuzzy Control of DFIG for Wind Energy Conversion Systems
title_sort Type-2 Fuzzy Control of DFIG for Wind Energy Conversion Systems
author Kouadria, Mohamed Abdeldjabbar
author_facet Kouadria, Mohamed Abdeldjabbar
Kouadria, Selman
Bouzid, Mohamed Amine
author_role author
author2 Kouadria, Selman
Bouzid, Mohamed Amine
author2_role author
author
dc.contributor.author.fl_str_mv Kouadria, Mohamed Abdeldjabbar
Kouadria, Selman
Bouzid, Mohamed Amine
description This study develops and designs a Type 2 fuzzy controller technique for application inwind turbines directly linked to the grid and incorporating variable-speed doubly fed inductiongenerators (DFIG).Type 2 fuzzy theory is proposed with the aim of enhancing system performance. Unlike Type 1 fuzzysystems, it accommodates a wide range of uncertainties and dynamic nonlinearities that mayconstrain the system's operational efficiency.Type 2 fuzzy logic provides an effective approach to managing linguistic uncertainty by modelingthe ambiguity and limited reliability of information, thereby reducing the overall level of uncertaintywithin the system.Both Type 1 Fuzzy Logic Control (T1FLC) and Type 2 Fuzzy Logic Control (T2FLC)techniques were employed in direct and indirect modes. The two control methods weredeveloped, their performances were evaluated, and the most effective control method interms of reference tracking and robustness was identified. This comparative analysis isderived from a series of tests performed under identical conditions during bothtransient and steady-state operations of the system.The simulation results demonstrate that the proposed method exhibits significant resilience toparameter variations and unstructured uncertainties.
publishDate 2025
dc.date.none.fl_str_mv 2025-06-26
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://itegam-jetia.org/journal/index.php/jetia/article/view/1640
10.5935/jetia.v11i53.1640
url https://itegam-jetia.org/journal/index.php/jetia/article/view/1640
identifier_str_mv 10.5935/jetia.v11i53.1640
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv https://itegam-jetia.org/journal/index.php/jetia/article/view/1640/1032
dc.rights.driver.fl_str_mv Copyright (c) 2025 ITEGAM-JETIA
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Copyright (c) 2025 ITEGAM-JETIA
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv ITEGAM - Instituto de Tecnologia e Educação Galileo da Amazônia
publisher.none.fl_str_mv ITEGAM - Instituto de Tecnologia e Educação Galileo da Amazônia
dc.source.none.fl_str_mv ITEGAM-JETIA; v.11 n.53 2025; 154-161
ITEGAM-JETIA; v.11 n.53 2025; 154-161
ITEGAM-JETIA; v.11 n.53 2025; 154-161
2447-0228
10.5935/jetia.v11i53
reponame:ITEGAM-JETIA
instname:Instituto de Tecnologia e Educação Galileo da Amazônia (ITEGAM)
instacron:ITEGAM
instname_str Instituto de Tecnologia e Educação Galileo da Amazônia (ITEGAM)
instacron_str ITEGAM
institution ITEGAM
reponame_str ITEGAM-JETIA
collection ITEGAM-JETIA
repository.name.fl_str_mv ITEGAM-JETIA - Instituto de Tecnologia e Educação Galileo da Amazônia (ITEGAM)
repository.mail.fl_str_mv editor@itegam-jetia.org
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