Speed Control of DFIG Wind Energy Conversion Using Horse Herd Optimization

Authors

  • Amira Lakhdar Badji Mokhtar Annaba University Author
  • Tahar Bahi Badji Mokhtar Annaba University Author
  • Amina Azizi Badji Mokhtar Annaba University Author
  • Amina Benabda Badji Mokhtar Annaba University Author

DOI:

https://doi.org/10.66950/qph9rm40

Keywords:

Wind Turbine, Doubly Fed Induction Generator, Horse Herd Optimization

Abstract

This paper proposes an intelligent rotor speed control strategy for a doubly fed induction generator wind energy conversion system based on the Horse Herd Optimization algorithm. The proposed method aims to maximize aerodynamic power extraction by maintaining the optimal tip speed ratio under variable wind conditions. A realistic multi-sinusoidal wind profile is used to represent both slow and rapid wind variations. The Horse Herd Optimization algorithm determines the optimal rotor speed that maximizes the mechanical power captured by the wind turbine. The performance of the proposed strategy is evaluated through MatLab simulations considering mechanical and electrical variables including rotor speed, mechanical power, stator and rotor voltages, currents, and grid active power. The results demonstrate fast convergence of the optimization algorithm, accurate rotor speed tracking, and stable power injection into the grid. These results confirm the effectiveness of the Horse Herd Optimization based control strategy for improving the performance of doubly fed induction generator wind energy conversion systems under fluctuating wind conditions.

References

(1) H. Badihi, Y. Zhang, B. Jiang, P. Pillay and S. Rakheja, "A Comprehensive Review on Signal-Based and Model-Based Condition Monitoring of Wind Turbines: Fault Diagnosis and Lifetime Prognosis," in Proceedings of the IEEE, vol. 110, no. 6, pp. 754-806, June 2022, doi: https://doi.org/10.1109/JPROC.2022.3171691. DOI: https://doi.org/10.1109/JPROC.2022.3171691

(2) Soleimani, Hamid, Daryoush Habibi, Mehrdad Ghahramani, Asma Aziz. 2024. "Strengthening Power Systems for Net Zero: A Review of the Role of Synchronous Condensers and Emerging Challenges" Energies 17, no. 13: 3291. https://doi.org/10.3390/en17133291 DOI: https://doi.org/10.3390/en17133291

(3) Boukadoum, A., Bouguerne, A., & Bahi, T. ,“Direct power control using space vector modulation strategy control for wind energy conversion system using three-phase matrix converter”. Electrical Engineering & Electromechanics, N° (3),(2023,pp. 40–46. https://doi.org/10.20998/2074-272X.2023.3.06 DOI: https://doi.org/10.20998/2074-272X.2023.3.06

(4) Yan S, Chen J, Wang M, et al. A Survey on Model Predictive Control of DFIGs in Wind Energy Conversion Systems. CSEE Journal of Power and Energy Systems, 2024, 10(3): 1085-1104. https://doi.org/10.17775/CSEEJPES.2022.08500 DOI: https://doi.org/10.17775/CSEEJPES.2022.08500

(5) Al-Betar, M.A., Awadallah, M.A., Braik, M.S. et al. Elk herd optimizer: a novel nature-inspired metaheuristic algorithm. Artif Intell Rev 57, 48 (2024). https://doi.org/10.1007/s10462-023-10680-4 DOI: https://doi.org/10.1007/s10462-023-10680-4

(6) Wang, L., Du, H., Zhang, Z. et al. Tianji’s horse racing optimization (THRO): a new metaheuristic inspired by ancient wisdom and its engineering optimization applications. Artif Intell Rev, Vol. 58, Art Numb.282 (2025). https://doi.org/10.1007/s10462-025-11269-9 DOI: https://doi.org/10.1007/s10462-025-11269-9

(7) Y. Djeriri, A. Meroufel, , A. Massoum, Z. Boudjema , "Comparative study between field oriented control strategy and direct power control strategy for DFIG". Journal of Electrical Engineering, 2014, Vol. 14, N°.2, https://api.semanticscholar.org/CorpusID:220480209

(8) Mohammed Kendzi, Abdelghani Aissaoui, “Comparison Between the MIT Rule and Fuzzy Logic Controller to Adapting the Power Generated by a Doubly Fed Induction Generator Integrated in a Wind System“.10th IEEE International Conference on Smart Grid,June 27-29, 2022, Istanbul, Turkey. https://doi.org/10.1109/icSmartGrid55722.2022.9848520 DOI: https://doi.org/10.1109/icSmartGrid55722.2022.9848520

(9) A.Lakhdara, T. Bahi, and A. Moussaoui. "Study and Management of an Hybrid System Connected to The Network." J. Electrical Systems Vol. 18, N°.2 (2022), pp.163-172.

(10) Mosayyebi, S.R., Shahalami, S.H. & Mojallali, H. Fault ride-through capability improvement in a DFIG-based wind turbine using modified ADRC. Prot Control Mod Power Syst 7, Art. Numb. 50 (2022). https://doi.org/10.1186/s41601-022-00272-9 DOI: https://doi.org/10.1186/s41601-022-00272-9

(11) Ansari, A. A., & Dyanamina, G. (2022). Fault Ride-Through Operation Analysis of Doubly Fed Induction Generator-Based Wind Energy Conversion Systems: A Comparative Review. Energies, Vol. 15, N°. 21, 8026. https://doi.org/10.3390/en15218026 DOI: https://doi.org/10.3390/en15218026

(12) Y. Soufi, T. Bahi, S. Lekhchine, D. Dib, Performance analysis of DFIM fed by matrix converter and multi level inverter, Energy conversion and management 72, 187-193, 2013.Volume 72, August 2013, pp. 187-193, https://doi.org/10.1016/j.enconman.2013.03.006 DOI: https://doi.org/10.1016/j.enconman.2013.03.006

(13) A. Lakhdara, T. Bahı, and A. Moussaoui, “Strategy of Sliding Mode Control Wind Energy Conversion System”, IJESA, vol. 4, no. 4, pp. 139–144, Dec. 2020, https://izlik.org/JA77ZB29LK

(14) M. Bourdoulis, and A. Alexandridis A, “ Rotor-side PI controller design of DFIG wind turbines based on direct power flow modeling,” 2nd Int Conf on Renewable Energy Research and Applications (ICRERA), Madrid, Spain., pp. 20-23, 2013. https://doi.org/10.1109/ICRERA.2013.6749743 DOI: https://doi.org/10.1109/ICRERA.2013.6749743

(15) Abdelkader Achar, Youcef Djeriri, Habib Benbouhenni, Riyadh Bouddou, Zakaria M. Salem Elbarbary, “Modified Vector-Controlled DFIG Wind Energy System Using Robust Model Predictive Rotor Current Control”, Arabian Journal for Science and Engineering, Vol. 50, 2024, Pp.11065 – 11089. DOI: https://doi.org/10.1007/s13369-024-09310-0

https://api.semanticscholar.org/CorpusID:271589192}

(16) Salman, M., Kashif, S.A.R., Fakhar, M.S. et al. Optimizing power generation in a hybrid solar wind energy system using a DFIG-based control approach. Sci Rep 15, 10550 (2025). https://doi.org/10.1038/s41598-025-95248-8 DOI: https://doi.org/10.1038/s41598-025-95248-8

(17) Amira Elkodama, Amr Ismaiel, A. Abdellatif, S. Shaaban, Shigeo Yoshida, Mostafa A. Rushdi, “Control methods for horizontal axis wind turbines (HAWT): State-of-the-art review", Energies Vol.16, Issue.17, Art. Numb. 6394. (2023). https://doi.org/10.3390/en16176394 DOI: https://doi.org/10.3390/en16176394

(18) Chenbo Wang; Zhiqiang Wu; Chaofan Ren; Zhonglin An; Zhonghua Gui; Weifu Luet al., "Electromagnetic Force Characteristics of Doubly-Fed Induction Machines Considering Harmonics from Rotor-Side Converters," in IEEE Transactions on Industry Applications, vol. 61, no. 5, pp. 7209-7219, Sept.-Oct. 2025, doi: https://doi.org/10.1109/TIA.2025.3568326. DOI: https://doi.org/10.1109/TIA.2025.3568326

(19) Darvishpoor, Shahin, Amirsalar Darvishpour, Mario Escarcega, and Mostafa Hassanalian "Nature-Inspired Algorithms from Oceans to Space: A Comprehensive Review of Heuristic and Meta-Heuristic Optimization Algorithms and Their Potential Applications in Drones" Drones , Vol. 7, no. 7: Art. Numb. 427.. 2023. https://doi.org/10.3390/drones7070427 DOI: https://doi.org/10.3390/drones7070427

(20) Balasubramaniam S, Seifedine Kadry, Manoj Kumar TK, K. Satheesh Kumar, “Bio-inspired Algorithms in Machine Learning and Deep Learning for Disease Detection”, 1st edition. CRC Press(2025). https://doi.org/10.1201/9781003538158 DOI: https://doi.org/10.1201/9781003538158

(21) Alghamdi, Ali S.. "Cost-Effective Planning of Hybrid Energy Systems Using Improved Horse Herd Optimizer and Cloud Theory under Uncertainty", Electronics Vol. 13, no. 13, Art Numb. 2471, 2024. https://doi.org/10.3390/electronics13132471 DOI: https://doi.org/10.3390/electronics13132471

(22) Duan, F., Basem, A., Jasim, D.J., Eslami M.& Okati M.,”A multi-objective improved horse herd optimizer based on convex lens imaging for stochastic optimization of wind energy resources in distribution networks considering reliability and uncertainty”. Scientific Reports Vol.14, Art. Numb. 29532 (2024). https://doi.org/10.1038/s41598-024-78977-0 DOI: https://doi.org/10.1038/s41598-024-78977-0

Downloads

Published

12-04-2026

How to Cite

Speed Control of DFIG Wind Energy Conversion Using Horse Herd Optimization. (2026). Journal of Elecetrical Engineering and Renewable Energies Development, 2(01), 1-10. https://doi.org/10.66950/qph9rm40