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Abstract

This paper develops a new nonlinear control law that outperforms the traditional sliding mode control law especially the suppression of the chatter phenomenon. This new controller design is an extension of the conventional sliding mode control law by transforming the sliding surface into a sliding sector which is divided into sliding sub-surfaces that define its boundaries. The fuzzy logic introduced by Takagi–Sugeno is integrated into this proposed controller instead of the traditional logic. This new control law will be applied to the two-mass model of the mechanical part of the wind power system to capture the maximum wind energy, minimize the vibrations of the shaft caused by the random fluctuation of the wind speed, maintain better performance in terms of stability demonstrated by the robust Lyapunov criterion, and reject the chatter phenomenon. The results of this new strategy are compared with both the traditional sliding mode control law and the recent switching sector control law and are illustrated by simulation results using MATLAB software.

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References

  1. Khan SA, Chowdhury MMH, Nandy U (2023) Solar-wind-power hybrid power generation system. J Eng Res Rep 25(10):145–152. https://doi.org/10.9734/jerr/2023/v25i101007

    Article  Google Scholar 

  2. Lee J, Bazilian M, Sovacool B, Hund K, Jowitt SM, Nguyen TP, Manberger A, Kah M, Greene S, Galeazzi C, Awuah-Offei K, Moats M, Tilton J, Kukoda S (2020) Reviewing the material and metal security of low-carbon energy transitions. Renew Sustain Energy Rev 124:109789. https://doi.org/10.1016/j.rser.2020.109789

    Article  Google Scholar 

  3. Berrada Y, El-Amrani A, Boumhidi I (2018) Feedback T-S Fuzzy controller in finite frequency for wind turbine. Green Energy Technol. https://doi.org/10.1007/978-981-13-1945-7_13

    Article  Google Scholar 

  4. Amimeur H, Aouzellag D, Abdessemed R, Ghedamsi K (2012) Sliding mode control of a dual-stator induction generator for wind energy conversion systems. Int J Electr Power Energy Syst 42(1):60–70. https://doi.org/10.1016/j.ijepes.2012.03.024

    Article  Google Scholar 

  5. Menezes EJN, Araújo AM, da Silva NSB (2017) A review on wind turbine control and its associated methods. J Clean Prod 174:945–953. https://doi.org/10.1016/j.jclepro.2017.10.297

    Article  Google Scholar 

  6. El Fadili Y, Berrada Y, Boumhidi I (2024) Novel control strategy for the global model of wind turbine. Int J Electr Comput Eng (IJECE) 14(1):258–267. https://doi.org/10.11591/ijece.v14i1.pp258-267

    Article  Google Scholar 

  7. Hand M, Balas MJ (1999) Non-linear and linear model based controller design for variable-speed wind turbines. In: Proceeding of the 3rd ASME/JSME joint fluids engineering conference, July

  8. Munteanu I, Cutululis NA, Bratcu AI, Ceangă E (2005) Optimization of variable speed wind power systems based on a LQG approach. Control Eng Pract 13(7):903–912. https://doi.org/10.1016/j.conengprac.2004.10.013

    Article  Google Scholar 

  9. Wu F, Zhang XP, Godfrey K, Ju P (2007) Small signal stability analysis and optimal control of a wind turbine with doubly fed induction generator. IET Gener Trans Distrib 1(5):751. https://doi.org/10.1049/iet-gtd:20060395

    Article  Google Scholar 

  10. Lahlou Z, Berrada Y, Boumhidi I (2019) Nonlinear feedback control for a complete wind energy conversion system. Int Rev Autom Control 12(3):136

    Google Scholar 

  11. Bayat F, Bahmani H (2017) Power regulation and control of wind turbines: LMI-based output feedback approach. Int Trans Electr Energy Syst. https://doi.org/10.1002/etep.2450

    Article  Google Scholar 

  12. Boumhidi I, Berrada Y (2021) Robust feedback controller in finite frequency based on H∞ performance for a variable speed wind turbine. Int J Powertrains 10(1):104–123

    Article  Google Scholar 

  13. El Fadili Y, Hmamed A, Boukili B, Boumhidi I (2022) Robust H performance of uncertain system based on Lyapunov functions using non–monotonic terms. In: 2022 10th international conference on systems and control (ICSC), Aix−Marseille University, Marseille, France, November

  14. Han B, Kong X, Zhang Z, Zhou L (2017) Neural network model predictive control optimisation for large wind turbines. IET Gener Trans Distrib 11(14):3491–3498. https://doi.org/10.1049/iet-gtd.2016.1989

    Article  Google Scholar 

  15. Morsi A, Abbas HS, Mohamed AM (2017) Wind turbine control based on a modified model predictive control scheme for linear parameter-varying systems. IET Control Theory Appl 11(17):3056–3068. https://doi.org/10.1049/iet-cta.2017.0426

    Article  MathSciNet  Google Scholar 

  16. Utkin V (1992) Sliding modes in control optimization. Springer, Berlin

    Book  Google Scholar 

  17. Utkin V (1973) Sliding modes in multidimensional systems with variable structure. In: IEEE conference on decision and control including the 12th symposium on adaptive processes-San Diego, CA, USA IEEE, pp 727–727. https://doi.org/10.1109/cdc.1973.269255

  18. Levant A (2003) Higher-order sliding modes, differentiation and output-feedback control. Int J Control 76(9–10):924–941. https://doi.org/10.1080/0020717031000099029

    Article  MathSciNet  Google Scholar 

  19. Berrada Y, Boufounas E, Boumhidi I (2015) Optimal neural network sliding mode control without reaching phase using genetic algorithm for a wind turbine. In: IEEE 2015 10th international conference on intelligent systems: theories and applications (SITA) – Rabat, pp 1–6. https://doi.org/10.1109/sita.2015.7358405

  20. Berrada Y, Boumhidi I (2017) Neural network sliding mode control with time-varying delay for a variable speed wind turbine. Int J Power Energy Convers 8(4):343–356

    Article  Google Scholar 

  21. Saravanakumar R, Jena D (2015) Validation of an integral sliding mode control for optimal control of a three blade variable speed variable pitch wind turbine. Int J Electr Power Energy Syst 69:421–429. https://doi.org/10.1016/j.ijepes.2015.01.031

    Article  Google Scholar 

  22. Bu-Lai W, Zi-Xin L, Ye-Cheng L, Jing-Heng Z (2023) Fuzzy sliding mode control of PMSM based on PSO. IEICE Electron Express 20(20):20230346. https://doi.org/10.1587/elex.20.20230346

    Article  Google Scholar 

  23. Utkin V, Lee H (2006) Chattering problem in sliding mode control systems. IFAC Proc Vol 39(5):1. https://doi.org/10.3182/20060607-3-IT-3902.00003

    Article  Google Scholar 

  24. Guo Y, Huang B, Guo J, Li A, Wang C (2018) Velocity-free sliding mode control for spacecraft with input saturation. Acta Astronaut. https://doi.org/10.1016/j.actaastro.2018.10.045

    Article  Google Scholar 

  25. Liu YH (2017) Saturated robust adaptive control for uncertain non-linear systems using a new approximate model. IET Control Theory Appl 11(6):870–876. https://doi.org/10.1049/iet-cta.2016.0979

    Article  MathSciNet  Google Scholar 

  26. Mercorelli P (2015) A two-stage sliding-mode high-gain observer to reduce uncertainties and disturbances effects for sensorless control in automotive applications. IEEE Trans Industr Electron 62(9):5929–5940. https://doi.org/10.1109/TIE.2015.2450725

    Article  Google Scholar 

  27. Evangelista C, Puleston P, Valenciaga F (2010) Wind turbine efficiency optimization. Comparative study of controllers based on second order sliding modes. Int J Hydrog Energy 35(11):5934–5939. https://doi.org/10.1016/j.ijhydene.2009.12.104

    Article  Google Scholar 

  28. Benahdouga S, Boukhetala D, Boudjema F (2012) Decentralized high order sliding mode control of multimachine power systems. Int J Electr Power Energy Syst 43(1):1081–1086

    Article  Google Scholar 

  29. Yang Y, Yan Y (2018) Backstepping sliding mode control for uncertain strict-feedback nonlinear systems using neural-network-based adaptive gain scheduling. J Syst Eng Electron 29(3):580–586. https://doi.org/10.2162/JSEE.2018.03.15

    Article  Google Scholar 

  30. Berrada Y, Boumhidi I (2020) New structure of sliding mode control for variable speed wind turbine. IFAC J Syst Control 14:100113. https://doi.org/10.1016/j.ifacsc.2020.100113

    Article  MathSciNet  Google Scholar 

  31. Neugebauer M, Akdeniz C, Demir V, Yurdem H (2023) Fuzzy logic control for watering system. Sci Rep. https://doi.org/10.1038/s41598-023-45203-2

    Article  Google Scholar 

  32. Filo G (2023) A review of Fuzzy logic method development in hydraulic and pneumatic systems. Energies 16(22):7584. https://doi.org/10.3390/en16227584

    Article  Google Scholar 

  33. Phan Bui K (2023) Application of Fuzzy logic in the robot control for mechanical processing. Vietnam J Sci Technol 61(4):531–572. https://doi.org/10.15625/2525-2518/18069

    Article  Google Scholar 

  34. Hassan I, Kar S (2023) The application of fuzzy logic techniques to improve decision making in apparel size. World J Adv Res Rev 19(02):607–615. https://doi.org/10.30574/wjarr.2023.19.2.1576

    Article  Google Scholar 

  35. Mamdani E (1977) Application of fuzzy logic to approximate reasoning using linguistic systems. Fuzzy Sets Syst 26:1182–1191

    Google Scholar 

  36. Witold P (1984) An identification algorithm in fuzzy relational systems. Fuzzy Sets Syst 13(2):153–167. https://doi.org/10.1016/0165-0114(84)90015-0

    Article  Google Scholar 

  37. Takagi T, Sugeno M (1985) Fuzzy identification of systems and its application to modeling and control. IEEE Trans Syst Man Cybern 15(1):116–132

    Article  Google Scholar 

  38. Chabani MS, Toufik BM, Amar G, Amel B, Iqbal M, Becherif M, Golea N (2023) Takagi-Sugeno Fuzzy logic controller for DFIG operating in the stand-alone mode: simulations and experimental investigation. Arab J Sci Eng. https://doi.org/10.1007/s13369-023-07704-0

    Article  Google Scholar 

  39. Polyakov A, Fridman L (2014) Stability notions and Lyapunov functions for sliding mode control systems. J Franklin Inst 351(4):1831–1865

    Article  MathSciNet  Google Scholar 

  40. Buhl ML (2003) SNwind user’s guide. National Wind Technology Center, National Renewable Energy Laboratory, 1 edn, Golden, Colorado

  41. El Fadili Y, Berrada Y, Boumhidi I (2023) Optimal controller design for wind turbine using sliding sector and genetic algorithms. E3S Web Conf 469(3):00006. https://doi.org/10.1051/e3sconf/202346900006

    Article  Google Scholar 

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Contributions

Yattou El Fadili: conceptualization, methodology, software, formal analysis, data curation, writing (original draft)–writing (review and editing). Youssef Berrada: conceptualization, methodology, software, formal analysis, data curation, writing (original draft)–writing (review and editing). Ismail Boumhidi: methodology, validation, data curation, writing (review & editing), supervision.

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Correspondence to Yattou El Fadili.

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El Fadili, Y., Berrada, Y. & Boumhidi, I. Improved sliding mode control law for wind power systems. Int. J. Dynam. Control (2024). https://doi.org/10.1007/s40435-024-01431-6

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