Advanced Sliding Mode Control Tuned with a PSO Algorithm for Wind Power Systems: Performance and Efficiency Enhancement

Document Type : Research paper

Author

AL-Furat AL-Awsat Technical University, Najaf, Iraq.

10.22098/joape.2025.17195.2350

Abstract

Wind energy systems based on Doubly-Fed Induction Generators are increasingly deployed due to their efficiency and cost-effectiveness. However, ensuring high power quality, system stability, and robustness under wind variability remains a challenge. This paper addresses these issues by proposing an improved Integral Sliding Mode controller, whose gains are optimized offline using the Particle Swarm Optimization algorithm. The control scheme regulates generator speed, DC-link voltage, and stator currents through a combined proportional-integral-sliding control law, reducing chattering while enhancing dynamic performance. The proposed ISM controller was implemented on both the rotor-side and grid-side converters of a DFIG-based wind energy conversion system. Simulation results under varying and extreme wind conditions confirm the superiority of the ISM controller over conventional PI control. Notably, the ISM reduced generator speed tracking error by over 75%, achieving a Root Mean Square Error of 1.06 rad/s. It also lowered stator current Total Harmonic Distortion to below 0.85%, improved turbine efficiency to 93.6%, and minimized electromagnetic torque ripple by more than 60%. In extreme wind conditions, the controller maintained stability and compliance with grid standards, with only minor degradation in performance. Overall, the proposed ISM controller demonstrates strong potential for improving power quality, reliability, and efficiency in modern wind power systems. Future work will explore adaptive gain tuning and experimental validation to further enhance real-time applicability and practical deployment in field conditions.

Keywords

Main Subjects

References

  1. D. Emara, M. Ezzat, A. Y. Abdelaziz, K. Mahmoud, M. Lehtonen, and M. M. Darwish, “Novel control strategy for enhancing microgrid operation connected to photovoltaic generation and energy storage systems,” Electron., vol. 10, no. 11, p. 1261, 2021.
  2. M. Aslan, B. Afif, M. Salmi, B. Merabet, M. Berka, and S. Masoud, “Performance enhancement of microgrid systems using backstepping control for grid side converter and mppt optimization,” Sol. Energy Sustain. Dev. J., vol. 14, no. 1, pp. 19–41, 2025.
  3. P. S. Manoj, A. Vijayakumari, and S. K. Kottayil, “Development of a comprehensive mppt for grid connected wind turbine driven pmsg,” Wind Energy, vol. 22, no. 6, pp. 732–744, 2019.
  4. J. Mohammadi, S. Vaez-Zadeh, S. Afsharnia, and E. Daryabeigi, “A combined vector and direct power control for dfig-based wind turbines,” IEEE Trans. Sustain. Energy, vol. 5, no. 3, pp. 767–775, 2014.
  5. C. Dardabi, A. Djebli, H. Chojaa, H. Aziz, A. Mouradi, M. A. Mossa, and T. A. Alghamdi, “Enhancing the control of doubly fed induction generators using artificial neural networks in the presence of real wind profiles,” PLoS One, vol. 19, no. 4, p. e0300527, 2024.
  6. Y. Dbaghi, S. Farhat, M. Mediouni, H. Essakhi, and A. Elmoudden, “Indirect power control of dfig based on wind turbine operating in mppt using backstepping approach,” Int. J. Electr. Comput. Eng., vol. 11, no. 3, p. 1951, 2021.
  7. F. Mazouz, S. Belkacem, I. Colak, S. Drid, and Y. Harbouche, “Adaptive direct power control for double fed induction generator used in wind turbine,” Int. J. Electr. Power Energy Syst., vol. 114, p. 105395, 2020.
  8. A. Younesi, S. Tohidi, and M. R. Feyzi, “Fixed switching frequency scheme for current predictive control of dfig,” J. Energy Manage. Technol., vol. 6, no. 2, pp. 73–82, 2022.
  9. A. Younesi, S. Tohidi, and M. R. Feyzi, “Computationally efficient long horizon model predictive direct current control of dfig wind turbines,” J. Oper. Autom. Power Eng., vol. 8, no. 2, pp. 172–181, 2020.
  10. Y. Bostani and S. Jalilzadeh, “A new approach based on wide-area fuzzy controller for damping of sub synchronous resonance in power system including dfig,” J. Oper. Autom. Power Eng., vol. 11, no. 1, pp. 61–68, 2023.
  11. M. Taoussi, M. Karim, B. Bossoufi, D. Hammoumi, A. Lagrioui, and A. Derouich, “Speed variable adaptive backstepping control of the doubly-fed induction machine drive,” Int. J. Autom. Control, vol. 10, no. 1, pp. 12–33, 2016.
  12. C. Hamid, A. Derouich, M. Taoussi, O. Zamzoum, and A. Hanafi, “An improved performance variable speed wind turbine driving a doubly fed induction generator using sliding mode strategy,” in 2020 IEEE 2nd Int. Conf. Electron. Control Optim. Comput. Sci., pp. 1–8, IEEE, Dec. 2020.
  13. T. A. Abderrazak, A. Iliace, B. M. Sofiane, B. R. Ilyas, and H. Hichem, “Transient stability of power in dfig wind farm through resilient with afrr, ida-pbc and pid control.” unpublished.
  14. K. Naresh, P. Reddy, and P. Sujatha, “Design and comparison of performance of dfig based wind turbine with pid controller, fuzzy controller, artificial neural network and model predictive controller,” EAI Endorsed Trans. Energy Web, vol. 9, no. 37, 2021.
  15. H. Chojaa, A. Derouich, S. E. Chehaidia, O. Zamzoum, M. Taoussi, and H. Elouatouat, “Integral sliding mode control for dfig based wecs with mppt based on artificial neural network under a real wind profile,” Energy Rep., vol. 7, pp. 4809–4824, 2021.
  16. J. Frqvxpswlq and L. Duh, “Welding consumables: State of the art and tendencies of development,” 2003.
  17. A. A. Hossam-Eldin, E. Negm, M. S. Elgamal, and K. M. AboRas, “Operation of grid-connected dfig using spwmand thipwm-based diode-clamped multilevel inverters,” IET Gener. Transm. Distrib., vol. 14, no. 8, pp. 1412–1419, 2020.
  18. K. Ouari, Y. Belkhier, H. Djouadi, A. Kasri, M. Bajaj, M. Alsharef, and S. Kamel, “Improved nonlinear generalized model predictive control for robustness and power enhancement of a dfig-based wind energy converter,” Front. Energy Res., vol. 10, p. 996206, 2022.
  19. M. Ali, S. M. Amrr, and M. Khalid, “Speed control of a wind turbine–driven doubly fed induction generator using sliding mode technique with practical finite-time stability,” Front. Energy Res., vol. 10, p. 970755, 2022.
  20. Y. Djeriri, “Robust second order sliding mode control of doubly-fed induction generator for wind energy conversion system,” Acta Electrotech. Inform., vol. 20, no. 3, pp. 30–38, 2020.
  21. D. Cherifi, Y. Miloud, and M. Mostefai, “High performance of direct power control for a doubly fed induction generator based on adaptive fuzzy second order sliding mode controller in wind energy conversion system,” Przegl. Elektrotech., no. 11, 2023.
  22. D. Cherifi and Y. Miloud, “Hybrid control using adaptive fuzzy sliding mode control of doubly fed induction generator for wind energy conversion system,” Period. Polytech. Electr. Eng. Comput. Sci., vol. 64, no. 4, pp. 374–381, 2020.
  23. R. Patel, F. Hafiz, A. Swain, and A. Ukil, “Nonlinear rotor side converter control of dfig based wind energy system,” Electr. Power Syst. Res., vol. 198, p. 107358, 2021.
  24. M. Amer, A. Miloudi, and F. Lakdja, “Optimal dtc control strategy of dfig using variable gain pi and hysteresis controllers adjusted by pso algorithm,” Period. Polytech. Electr. Eng. Comput. Sci., vol. 64, no. 1, pp. 74–86, 2020.
  25. A. Younesi, S. Tohidi, and M. R. Feyzi, “An improved long-horizon model predictive control for dfig in wecs with variable sampling-time,” IET Renew. Power Gener., vol. 16, no. 3, pp. 517–531, 2022.
  26. A. Ullah, S. Ullah, T. U. u. Rahman, I. Sami, A. U. u. Rahman, B. Alghamdi, and J. Pan, “Enhanced wind energy conversion system performance using fast smooth secondorder sliding mode control with neuro-fuzzy estimation and variable-gain robust exact output differentiator,” Appl. Energy, vol. 377, p. 124364, 2025.
  27. B. Ibrahim, H. Abdelkader, M. A. Hartani, and K. Kayisli, “Optimization of sliding mode control for doubly fed induction generator systems using particle swarm and grey wolf algorithms,” Electr. Power Compon. Syst., vol. 52,
    no. 10, pp. 1782–1795, 2024.
  28. H. Laina, S. Bougdour, B. Sefriti, S. Sefriti, and I. Boumhidi, “Optimal sliding mode control based on particle swarm optimization for wind turbine,” in 2024 Sixth Int. Conf. Intell. Comput. Data Sci., pp. 1–8, IEEE, 2024.
  29. P. Parimala and P. N. Babu, “Advanced control and optimization techniques for dfig wind energy systems: A case study using pelican algorithm and ctid-pid controller,” AIP Adv., vol. 15, no. 6, 2025.
  30. G. S. Bruno, D.-E. A. Mansour, A. Nada, and T. F. Megahed, “Hybrid ann-based mppt control for dfig wind systems using type-2 fuzzy logic and super-twisting sliding mode control,” Smart Grids Sustain. Energy, vol. 10, no. 2, p. 32, 2025.
  31. H. Itouchene, Z. Boudries, and F. Amrane, “Improved power control based variable speed wind-turbine dfig under hard work conditions: Application of sliding mode theory,” Period. Polytech. Electr. Eng. Comput. Sci., vol. 68, no. 4, pp. 392–412, 2024.
  32. M. Salman, S. A. R. Kashif, M. S. Fakhar, A. Rasool, and A. S. Hussen, “Optimizing power generation in a hybrid solar wind energy system using a dfig-based control approach,” Sci. Rep., vol. 15, no. 1, p. 10550, 2025.
  33. Z. Faramarzi, S. Abazari, S. Hoghoughi, and N. Abjadi, “Improved power system dynamic stability by dfig in the presence of sssc using adaptive nonlinear multi-input backstepping,” J. Oper. Autom. Power Eng., vol. 12, no. 2, pp. 107–120, 2024.
  34. Y. Atifi, M. Kissaoui, A. Raihani, K. Errakkas, and A. Khayat, “Advanced nonlinear control of wec system in ac microgrid connected to the main grid with electric vehicle integration,” e-Prime–Adv. Electr. Eng. Electron. Energy, vol. 9, p. 100718, 2024.
  35. F. Taibi, O. Benzineb, M. Tadjine, M. S. Boucherit, and M. E. H. Benbouzid, “Hybrid sliding mode control of dfig with mppt using three multicellular converters,” in IFAC Proc. Vol., vol. 47, pp. 11659–11666, 2014.
  36. M. ElAzzaoui, H. Mahmoudi, C. Ed-dahmani, and K. Boudaraia, “Comparing performance of pi and sliding mode in control of grid connected doubly fed induction generator,” in 2016 Int. Renew. Sustain. Energy Conf., pp. 769–774, IEEE, 2016.
  37. V. Pande, U. Mate, and S. Kurode, “Discrete sliding mode control strategy for direct real and reactive power regulation of wind driven dfig,” Electr. Power Syst. Res., vol. 100, pp. 73–81, 2013.
  38. A. Achar, Y. Djeriri, H. Benbouhenni, R. Bouddou, and Z. Elbarbary, “Modified vector-controlled dfig wind energy system using robust model predictive rotor current control,” Arab. J. Sci. Eng., pp. 1–25, 2024.
  39. R. Shanmugam, D. K. Sakthivel, A. N. Ramaiah, and S. Ramalingam, “Nonlinear control strategy for dc-link voltage control in a dfig of wecs during 3φ grid faults,” IEEE Trans. Ind. Electron., vol. 71, no. 10, pp. 12468–12475, 2024.
  40. A. El Ouali, Y. Lakhal, M. Benchagra, H. Chojaa, M. V. O. Mohamed, A. Maarif, and M. A. Mossa, “Comparative study of linear and nonlinear controllers for dfig-based wind power systems under different operating conditions,” J. Rob. Control, vol. 6, no. 3, pp. 1208–1215, 2025.
  41. M. J. Hossain, T. K. Saha, N. Mithulananthan, and H. R. Pota, “Control strategies for augmenting lvrt capability of dfigs in interconnected power systems,” IEEE Trans. Ind. Electron., vol. 60, no. 6, pp. 2510–2522, 2012.
Journal of Operation and Automation in Power Engineering
Volume 12, Special Issue
Advanced Technologies for Resilient and Efficient Microgrid Management: Innovations in Energy Optimization, Security, and Integration
2024
Pages 89-99

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History

  • Receive Date: 16 April 2025
  • Revise Date: 10 June 2025
  • Accept Date: 18 July 2025
  • First Publish Date: 18 July 2025

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