Grey wolf optimization for enhanced performance in wind power system with dual-star induction generators
This study investigates strategies for enhancing the performance of dual-star induction generators in wind power systems by optimizing the full control algorithm. The control mechanisms involved include the PID (Proportional-Integral-Derivative) controller for speed regulation and the PI (Proportion...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2024-10-01
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| Series: | Frontiers in Energy Research |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fenrg.2024.1421336/full |
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| author | Katia Benamara Hocine Amimeur Yanis Hamoudi Maher G. M. Abdolrasol Umit Cali Umit Cali Taha Selim Ustun |
| author_facet | Katia Benamara Hocine Amimeur Yanis Hamoudi Maher G. M. Abdolrasol Umit Cali Umit Cali Taha Selim Ustun |
| author_sort | Katia Benamara |
| collection | DOAJ |
| description | This study investigates strategies for enhancing the performance of dual-star induction generators in wind power systems by optimizing the full control algorithm. The control mechanisms involved include the PID (Proportional-Integral-Derivative) controller for speed regulation and the PI (Proportional-Integral) controller for flux, DC-link voltage, and grid connection control. The primary objective is to optimize the entire system by fine-tuning PID and PI controllers through the application of meta-heuristic algorithms, specifically Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO). These algorithms play a crucial role in estimating the optimal values of Kp, Ki, and Kd for the PID speed controller, as well as Kp and Ki for the PI controller used in the flux, DC-link voltage, and grid connection for wind energy conversion system based dual-star induction generator. This comprehensive optimization ensures accurate parameter tuning for optimal system performance. A comparative analysis of the optimization results has been conducted, focusing on the outcomes obtained with the GWO algorithm. The findings reveal a notable reduction in steady-state error, signifying improved stability, and an overall enhancement in the wind power system’s performance. This study contributes valuable insights into the effective application of meta-heuristic algorithms for optimizing dual-star induction generators in wind power systems. |
| format | Article |
| id | doaj-art-2762003c7c0b4034b244c45c06f30e2a |
| institution | OA Journals |
| issn | 2296-598X |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Energy Research |
| spelling | doaj-art-2762003c7c0b4034b244c45c06f30e2a2025-08-20T01:50:45ZengFrontiers Media S.A.Frontiers in Energy Research2296-598X2024-10-011210.3389/fenrg.2024.14213361421336Grey wolf optimization for enhanced performance in wind power system with dual-star induction generatorsKatia Benamara0Hocine Amimeur1Yanis Hamoudi2Maher G. M. Abdolrasol3Umit Cali4Umit Cali5Taha Selim Ustun6Laboratoire de Maitrise des Energies Renouvelables, Faculté de Technologie, Université de Bejaia, Bejaia, AlgeriaLaboratoire de Maitrise des Energies Renouvelables, Faculté de Technologie, Université de Bejaia, Bejaia, AlgeriaLaboratoire de Maitrise des Energies Renouvelables, Faculté de Technologie, Université de Bejaia, Bejaia, AlgeriaInstitute of Sustainable Energy, Universiti Tenaga Nasional, Kajang, MalaysiaDepartment of Electric Energy, Norwegian University of Science and Technology, Trondheim, NorwaySchool of Physics, Engineering and Technology, University of York, York, United KingdomFukushima Renewable Energy Institute, National Institute of Advanced Industrial Science and Technology (AIST), Koriyama, JapanThis study investigates strategies for enhancing the performance of dual-star induction generators in wind power systems by optimizing the full control algorithm. The control mechanisms involved include the PID (Proportional-Integral-Derivative) controller for speed regulation and the PI (Proportional-Integral) controller for flux, DC-link voltage, and grid connection control. The primary objective is to optimize the entire system by fine-tuning PID and PI controllers through the application of meta-heuristic algorithms, specifically Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO). These algorithms play a crucial role in estimating the optimal values of Kp, Ki, and Kd for the PID speed controller, as well as Kp and Ki for the PI controller used in the flux, DC-link voltage, and grid connection for wind energy conversion system based dual-star induction generator. This comprehensive optimization ensures accurate parameter tuning for optimal system performance. A comparative analysis of the optimization results has been conducted, focusing on the outcomes obtained with the GWO algorithm. The findings reveal a notable reduction in steady-state error, signifying improved stability, and an overall enhancement in the wind power system’s performance. This study contributes valuable insights into the effective application of meta-heuristic algorithms for optimizing dual-star induction generators in wind power systems.https://www.frontiersin.org/articles/10.3389/fenrg.2024.1421336/fullfield oriented controldual star induction generatorgrey wolf optimizationparticle swarm optimizationwind energy |
| spellingShingle | Katia Benamara Hocine Amimeur Yanis Hamoudi Maher G. M. Abdolrasol Umit Cali Umit Cali Taha Selim Ustun Grey wolf optimization for enhanced performance in wind power system with dual-star induction generators Frontiers in Energy Research field oriented control dual star induction generator grey wolf optimization particle swarm optimization wind energy |
| title | Grey wolf optimization for enhanced performance in wind power system with dual-star induction generators |
| title_full | Grey wolf optimization for enhanced performance in wind power system with dual-star induction generators |
| title_fullStr | Grey wolf optimization for enhanced performance in wind power system with dual-star induction generators |
| title_full_unstemmed | Grey wolf optimization for enhanced performance in wind power system with dual-star induction generators |
| title_short | Grey wolf optimization for enhanced performance in wind power system with dual-star induction generators |
| title_sort | grey wolf optimization for enhanced performance in wind power system with dual star induction generators |
| topic | field oriented control dual star induction generator grey wolf optimization particle swarm optimization wind energy |
| url | https://www.frontiersin.org/articles/10.3389/fenrg.2024.1421336/full |
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