Type-2 Fuzzy Control of DFIG for Wind Energy Conversion Systems
This study develops and designs a Type 2 fuzzy controller technique for application in wind turbines directly linked to the grid and incorporating variable-speed doubly fed induction generators (DFIG). Type 2 fuzzy theory is proposed with the aim of enhancing system performance. Unlike Type 1 fuzzy...
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| Format: | Article |
| Language: | English |
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Institute of Technology and Education Galileo da Amazônia
2025-06-01
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| Series: | ITEGAM-JETIA |
| Online Access: | http://itegam-jetia.org/journal/index.php/jetia/article/view/1640 |
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| _version_ | 1839650653637443584 |
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| author | Mohamed Abdeldjabbar Kouadria Selman Kouadria Mohamed Amine Bouzid |
| author_facet | Mohamed Abdeldjabbar Kouadria Selman Kouadria Mohamed Amine Bouzid |
| author_sort | Mohamed Abdeldjabbar Kouadria |
| collection | DOAJ |
| description |
This study develops and designs a Type 2 fuzzy controller technique for application in
wind turbines directly linked to the grid and incorporating variable-speed doubly fed induction
generators (DFIG).
Type 2 fuzzy theory is proposed with the aim of enhancing system performance. Unlike Type 1 fuzzy
systems, it accommodates a wide range of uncertainties and dynamic nonlinearities that may
constrain the system's operational efficiency.
Type 2 fuzzy logic provides an effective approach to managing linguistic uncertainty by modeling
the ambiguity and limited reliability of information, thereby reducing the overall level of uncertainty
within 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 were
developed, their performances were evaluated, and the most effective control method in
terms of reference tracking and robustness was identified. This comparative analysis is
derived from a series of tests performed under identical conditions during both
transient and steady-state operations of the system.
The simulation results demonstrate that the proposed method exhibits significant resilience to
parameter variations and unstructured uncertainties.
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| format | Article |
| id | doaj-art-1c0021d340c842a495c38afdd2a19f33 |
| institution | Matheson Library |
| issn | 2447-0228 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Institute of Technology and Education Galileo da Amazônia |
| record_format | Article |
| series | ITEGAM-JETIA |
| spelling | doaj-art-1c0021d340c842a495c38afdd2a19f332025-06-27T01:07:11ZengInstitute of Technology and Education Galileo da AmazôniaITEGAM-JETIA2447-02282025-06-01115310.5935/jetia.v11i53.1640Type-2 Fuzzy Control of DFIG for Wind Energy Conversion SystemsMohamed Abdeldjabbar Kouadria0Selman Kouadria1Mohamed Amine Bouzid2Department of Electrical Engineering Abdelhamid Ibn Badis University Mostaganem, Algérie.Laboratoire de Génie électrique et des plasmas, Université Ibn Khaldoun Tiaret, AlgérieLaboratory Computer Engineering and Energy Engineering Ibn Khaldoun University, Algérie. This study develops and designs a Type 2 fuzzy controller technique for application in wind turbines directly linked to the grid and incorporating variable-speed doubly fed induction generators (DFIG). Type 2 fuzzy theory is proposed with the aim of enhancing system performance. Unlike Type 1 fuzzy systems, it accommodates a wide range of uncertainties and dynamic nonlinearities that may constrain the system's operational efficiency. Type 2 fuzzy logic provides an effective approach to managing linguistic uncertainty by modeling the ambiguity and limited reliability of information, thereby reducing the overall level of uncertainty within 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 were developed, their performances were evaluated, and the most effective control method in terms of reference tracking and robustness was identified. This comparative analysis is derived from a series of tests performed under identical conditions during both transient and steady-state operations of the system. The simulation results demonstrate that the proposed method exhibits significant resilience to parameter variations and unstructured uncertainties. http://itegam-jetia.org/journal/index.php/jetia/article/view/1640 |
| spellingShingle | Mohamed Abdeldjabbar Kouadria Selman Kouadria Mohamed Amine Bouzid Type-2 Fuzzy Control of DFIG for Wind Energy Conversion Systems ITEGAM-JETIA |
| title | 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_short | Type-2 Fuzzy Control of DFIG for Wind Energy Conversion Systems |
| title_sort | type 2 fuzzy control of dfig for wind energy conversion systems |
| url | http://itegam-jetia.org/journal/index.php/jetia/article/view/1640 |
| work_keys_str_mv | AT mohamedabdeldjabbarkouadria type2fuzzycontrolofdfigforwindenergyconversionsystems AT selmankouadria type2fuzzycontrolofdfigforwindenergyconversionsystems AT mohamedaminebouzid type2fuzzycontrolofdfigforwindenergyconversionsystems |