Type-2 Fuzzy Control of DFIG for Wind Energy Conversion Systems
| Autor(a) principal: | |
|---|---|
| Data de Publicação: | 2025 |
| Outros Autores: | , |
| 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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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 |
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Copyright (c) 2025 ITEGAM-JETIA |
| eu_rights_str_mv |
openAccess |
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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 |
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Instituto de Tecnologia e Educação Galileo da Amazônia (ITEGAM) |
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ITEGAM |
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ITEGAM |
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ITEGAM-JETIA |
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ITEGAM-JETIA |
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ITEGAM-JETIA - Instituto de Tecnologia e Educação Galileo da Amazônia (ITEGAM) |
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editor@itegam-jetia.org |
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1837010820315217920 |