Experimental evaluation of an advanced predictive control technique for variable-speed wind turbine systems
Wind energy control plays a crucial role in optimizing the performance of Doubly Fed Induction Generators (DFIGs) by maximizing power extraction while ensuring stable grid integration. To achieve this, a Maximum Power Point Tracking (MPPT) strategy is employed to determine the optimal mechanical spe...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-07-01
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| Series: | International Journal of Electrical Power & Energy Systems |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0142061525002194 |
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| author | Farah Echiheb Btissam Majout Ismail EL Kafazi Badre Bossoufi Abdelhamid Rabhi Nicu Bizon Anton Zhilenkov Saleh Mobayen |
| author_facet | Farah Echiheb Btissam Majout Ismail EL Kafazi Badre Bossoufi Abdelhamid Rabhi Nicu Bizon Anton Zhilenkov Saleh Mobayen |
| author_sort | Farah Echiheb |
| collection | DOAJ |
| description | Wind energy control plays a crucial role in optimizing the performance of Doubly Fed Induction Generators (DFIGs) by maximizing power extraction while ensuring stable grid integration. To achieve this, a Maximum Power Point Tracking (MPPT) strategy is employed to determine the optimal mechanical speed and reference power, enabling efficient wind energy conversion. However, maintaining precise control over the active and reactive power exchange remains a challenge, especially under varying operating conditions. This paper presents an experimental study on the application of deadbeat predictive control to a DFIG-based wind energy system, integrating MPPT for optimal power tracking. The study, conducted using a DSPACE DS1104 test bench, includes the development of a comprehensive mathematical model, an analysis of the deadbeat control strategy, and its implementation in MATLAB/Simulink. Experimental validation demonstrates that the proposed control method achieves faster response time (0.0504 s), reduced Total Harmonic Distortion (THD) to 0.5 %, and enhanced robustness against parameter variations, ensuring both maximum power extraction and high-quality power injection into the grid. These results confirm the superiority of the MPPT-integrated deadbeat predictive control over conventional methods in terms of efficiency, power quality, and system stability. However, while this method shows promising results, its implementation in real-world, large-scale systems requires further investigation to address challenges such as grid stability under fluctuating conditions and the scalability of the control strategy. In terms of practical implications, the proposed control method offers potential for improving the performance and efficiency of DFIG-based wind energy systems, contributing to more sustainable and reliable energy production. The research also holds social implications by advancing renewable energy technologies, which are essential for reducing dependency on fossil fuels and mitigating the effects of climate change. |
| format | Article |
| id | doaj-art-0df03412849e42c5a049e27b2b004b5f |
| institution | OA Journals |
| issn | 0142-0615 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Electrical Power & Energy Systems |
| spelling | doaj-art-0df03412849e42c5a049e27b2b004b5f2025-08-20T01:51:54ZengElsevierInternational Journal of Electrical Power & Energy Systems0142-06152025-07-0116811066810.1016/j.ijepes.2025.110668Experimental evaluation of an advanced predictive control technique for variable-speed wind turbine systemsFarah Echiheb0Btissam Majout1Ismail EL Kafazi2Badre Bossoufi3Abdelhamid Rabhi4Nicu Bizon5Anton Zhilenkov6Saleh Mobayen7LIMAS Laboratory, Faculty of Sciences Dhar El Mahraz, Sidi Mohammed Ben Abdellah University Fez, Morocco; SMARTilab Laboratory, Moroccan School of Engineering Sciences Rabat, MoroccoLIMAS Laboratory, Faculty of Sciences Dhar El Mahraz, Sidi Mohammed Ben Abdellah University Fez, MoroccoSMARTilab Laboratory, Moroccan School of Engineering Sciences Rabat, MoroccoLIMAS Laboratory, Faculty of Sciences Dhar El Mahraz, Sidi Mohammed Ben Abdellah University Fez, Morocco; Corresponding authors at: Graduate School of Intelligent Data Science, National Yunlin University of Science and Technology, Douliou 640301, Yunlin, Taiwan (F. Mobayen).MIS Laboratory, University of Picardie Jules Verne, 80000 Amiens, FranceThe National University of Science and Technology Politechnica Bucharest, Pitești University Centre, 110040 Pitesti, RomaniaDepartment of Cyber-Physical Systems, St. Petersburg State Marine Technical University, 190121 Saint-Petersburg, RussiaGraduate School of Intelligent Data Science, National Yunlin University of Science and Technology, Douliou 640301 Yunlin, Taiwan; Energy Systems Research Center, Khazar university, Mahasti str. 41, Baku AZ1096, Azerbaijan; Corresponding authors at: Graduate School of Intelligent Data Science, National Yunlin University of Science and Technology, Douliou 640301, Yunlin, Taiwan (F. Mobayen).Wind energy control plays a crucial role in optimizing the performance of Doubly Fed Induction Generators (DFIGs) by maximizing power extraction while ensuring stable grid integration. To achieve this, a Maximum Power Point Tracking (MPPT) strategy is employed to determine the optimal mechanical speed and reference power, enabling efficient wind energy conversion. However, maintaining precise control over the active and reactive power exchange remains a challenge, especially under varying operating conditions. This paper presents an experimental study on the application of deadbeat predictive control to a DFIG-based wind energy system, integrating MPPT for optimal power tracking. The study, conducted using a DSPACE DS1104 test bench, includes the development of a comprehensive mathematical model, an analysis of the deadbeat control strategy, and its implementation in MATLAB/Simulink. Experimental validation demonstrates that the proposed control method achieves faster response time (0.0504 s), reduced Total Harmonic Distortion (THD) to 0.5 %, and enhanced robustness against parameter variations, ensuring both maximum power extraction and high-quality power injection into the grid. These results confirm the superiority of the MPPT-integrated deadbeat predictive control over conventional methods in terms of efficiency, power quality, and system stability. However, while this method shows promising results, its implementation in real-world, large-scale systems requires further investigation to address challenges such as grid stability under fluctuating conditions and the scalability of the control strategy. In terms of practical implications, the proposed control method offers potential for improving the performance and efficiency of DFIG-based wind energy systems, contributing to more sustainable and reliable energy production. The research also holds social implications by advancing renewable energy technologies, which are essential for reducing dependency on fossil fuels and mitigating the effects of climate change.http://www.sciencedirect.com/science/article/pii/S0142061525002194Doubly Fed Induction Generator DFIGFiel Oriented Control FOCModel Predictive Control MPC DeadbeatWind Energy Conversion System WECSMaximum power point tracking MPPT |
| spellingShingle | Farah Echiheb Btissam Majout Ismail EL Kafazi Badre Bossoufi Abdelhamid Rabhi Nicu Bizon Anton Zhilenkov Saleh Mobayen Experimental evaluation of an advanced predictive control technique for variable-speed wind turbine systems International Journal of Electrical Power & Energy Systems Doubly Fed Induction Generator DFIG Fiel Oriented Control FOC Model Predictive Control MPC Deadbeat Wind Energy Conversion System WECS Maximum power point tracking MPPT |
| title | Experimental evaluation of an advanced predictive control technique for variable-speed wind turbine systems |
| title_full | Experimental evaluation of an advanced predictive control technique for variable-speed wind turbine systems |
| title_fullStr | Experimental evaluation of an advanced predictive control technique for variable-speed wind turbine systems |
| title_full_unstemmed | Experimental evaluation of an advanced predictive control technique for variable-speed wind turbine systems |
| title_short | Experimental evaluation of an advanced predictive control technique for variable-speed wind turbine systems |
| title_sort | experimental evaluation of an advanced predictive control technique for variable speed wind turbine systems |
| topic | Doubly Fed Induction Generator DFIG Fiel Oriented Control FOC Model Predictive Control MPC Deadbeat Wind Energy Conversion System WECS Maximum power point tracking MPPT |
| url | http://www.sciencedirect.com/science/article/pii/S0142061525002194 |
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