Skip to main content

Advertisement

SpringerLink
Log in

Design nonlinear feedback strategy using H2/H∞ control and neural network based estimator for variable speed wind turbine

  • Published:
International Journal of Dynamics and Control Aims and scope Submit manuscript

Abstract

In this paper, a hybrid approach based on robust control and radial basis function network has been proposed to adjust the output power and generator speed of a variable speed wind turbine. The system is composed of four characteristics: aerodynamics, turbine mechanism, generator dynamics, and actuator dynamics. Such a system has high nonlinearity essence and involves high dynamic mutation. To guarantee the robust stability of internal dynamics, the linearized model of wind turbine has been derived around its operating points. Afterward, H2/H∞ state feedback law has been determined by solving some linear matrix inequalities. Moreover, to keep the system close to its working conditions, a nonlinear compensator has been designed. For instant estimation of the nonlinearity of wind turbines, RBF has been used through an online learning procedure. The performance of the proposed algorithm has been assessed because of internal stability, tracking performance, and eliminating the system’s nonlinearities in the presence of wind with fixed and variable speed.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24
Fig. 25
Fig. 26
Fig. 27
Fig. 28
Fig. 29
Fig. 30
Fig. 31

Similar content being viewed by others

Abbreviations

η:

Learning rate of RBF

λ 1,2,3,4 > 0:

Designing parameters of the control signals

γ :

Upper bound of infinity-norm of closed-loop transfer function between the uncertainty and measured output

υ :

Upper bound of 2-norm of closed-loop transfer function between the uncertainty and measured output

References

  1. Menezes EJN, Araújo AM, da Silva NSB (2018) A review on wind turbine control and its associated methods. J Clean Prod 174:945–953

    Article  Google Scholar 

  2. Bórawski P, Bełdycka-Bórawska A, Jankowski KJ, Dubis B, Dunn JW (2020) Development of wind energy market in the European Union. Renew Energy 161:691–700

    Article  Google Scholar 

  3. Bórawski P, Bełdycka-Bórawska A, Szymańska EJ, Jankowski KJ, Dubis B, Dunn JW (2019) Development of renewable energy sources market and biofuels in the European Union. J Clean Prod 228:467–484

    Article  Google Scholar 

  4. Tan M, Zhang Z (2016) Wind turbine modeling with data-driven methods and radially uniform designs. IEEE Trans Ind Inf 12(3):1261–1269

    Article  Google Scholar 

  5. Ouyang T, Kusiak A, He Y (2017) Modeling wind-turbine power curve: a data partitioning and mining approach. Renew Energy 102:1–8

    Article  Google Scholar 

  6. Yang B, Zhang X, Yu T, Shu H, Fang Z (2017) Grouped grey wolf optimizer for maximum power point tracking of doubly-fed induction generator based wind turbine. Energy Convers Manag 133:427–443

    Article  Google Scholar 

  7. Mokhtari Y, Rekioua D (2018) High performance of maximum power point tracking using ant colony algorithm in wind turbine. Renew Energy 126:1055–1063

    Article  Google Scholar 

  8. Huynh P, Tungare S, Banerjee A (2020) Maximum power point tracking for wind turbine using integrated generator-rectifier systems. IEEE Trans Power Electron 36(1):504–512

    Article  Google Scholar 

  9. Colombo L, Corradini ML, Ippoliti G, Orlando G (2020) Pitch angle control of a wind turbine operating above the rated wind speed: a sliding mode control approach. ISA Trans 96:95–102

    Article  Google Scholar 

  10. Tang X, Yin M, Shen C, Xu Y, Dong ZY, Zou Y (2018) Active power control of wind turbine generators via coordinated rotor speed and pitch angle regulation. IEEE Trans Sustain Energy 10(2):822–832

    Article  Google Scholar 

  11. Lasheen A, Elnaggar M, Yassin H (2020) Adaptive control design and implementation for collective pitch in wind energy conversion systems. ISA Trans 102:251–263

    Article  Google Scholar 

  12. Sahoo S, Subudhi B, Panda G (2020) Torque and pitch angle control of a wind turbine using multiple adaptive neuro-fuzzy control. Wind Eng 44(2):125–141

    Article  Google Scholar 

  13. Gnaneswaran N, Joo YH (2019) Event-triggered stabilisation for T–S fuzzy systems with asynchronous premise constraints and its application to wind turbine system. IET Control Theory Appl 13(10):1532–1542

    Article  MathSciNet  Google Scholar 

  14. Asgharnia A, Shahnazi R, Jamali A (2018) Performance and robustness of optimal fractional fuzzy PID controllers for pitch control of a wind turbine using chaotic optimization algorithms. ISA Trans 79:27–44

    Article  Google Scholar 

  15. Yuan Y, Chen X, Tang J (2020) Multivariable robust blade pitch control design to reject periodic loads on wind turbines. Renew Energy 146:329–341

    Article  Google Scholar 

  16. Yin W, Wu X, Rui X (2018) Adaptive robust backstepping control of the speed regulating differential mechanism for wind turbines. IEEE Trans Sustain Energy 10(3):1311–1318

    Article  Google Scholar 

  17. Das S, Subudhi B (2018) A H∞ robust active and reactive power control scheme for a PMSG-based wind energy conversion system. IEEE Trans Energy Convers 33(3):980–990

    Article  Google Scholar 

  18. Barambones O (2019) Robust wind speed estimation and control of variable speed wind turbines. Asian J Control 21(2):856–867

    Article  MathSciNet  Google Scholar 

  19. Zaafouri C, Torchani B, Sellami A, Garcia G (2018) Uncertain saturated discrete-time sliding mode control for a wind turbine using a two-mass model. Asian J Control 20(2):802–818

    Article  MathSciNet  Google Scholar 

  20. Tahir K, Belfedal C, Allaoui T, Denai M, Doumi MH (2018) A new sliding mode control strategy for variable-speed wind turbine power maximization. Int Trans Electr Energy Syst 28(4):e2513

    Article  Google Scholar 

  21. Zargham F, Mazinan AH (2019) Super-twisting sliding mode control approach with its application to wind turbine systems. Energy Syst 10(1):211–229

    Article  Google Scholar 

  22. Hwang S, Park JB, Joo YH (2019) Disturbance observer-based integral fuzzy sliding-mode control and its application to wind turbine system. IET Control Theory Appl 13(12):1891–1900

    Article  MathSciNet  Google Scholar 

  23. Abolvafaei M, Ganjefar S (2019) Maximum power extraction from a wind turbine using second-order fast terminal sliding mode control. Renew Energy 139:1437–1446

    Article  Google Scholar 

  24. Golnary F, Moradi H (2018) Design and comparison of quasi continuous sliding mode control with feedback linearization for a large scale wind turbine with wind speed estimation. Renew Energy 127:495–508

    Article  Google Scholar 

  25. Lan J, Patton RJ, Zhu X (2018) Fault-tolerant wind turbine pitch control using adaptive sliding mode estimation. Renew Energy 116:219–231

    Article  Google Scholar 

  26. Thomsen SC (2006) Nonlinear control of a wind turbine (Master's thesis, Technical University of Denmark, DTU, DK-2800 Kgs. Lyngby, Denmark)

  27. Gu DW, Petkov P, Konstantinov MM (2005) Robust control design with MATLAB®. Springer

  28. Yang CD, Sun YP (2001) Mixed H2/H cruise controller design for high speed train. Int J Control 74(9):905–920

    Article  Google Scholar 

  29. https://yalmip.github.io

  30. Utkin V, Poznyak A, Orlov Y, Polyakov A (2020) Conventional and high order sliding mode control. J Frank Inst 357(15): 10244–10261

    Article  MathSciNet  Google Scholar 

Download references

Funding

Not applicable.

Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Islamic Azad University Tehran North Branch, Tehran, Iran

    Alireza Ranjineh Khojasteh & Hamid Toshani

  2. Department of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran

    Alireza Ranjineh Khojasteh & Hamid Toshani

Authors
  1. Alireza Ranjineh Khojasteh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hamid Toshani
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Alireza Ranjineh Khojasteh.

Ethics declarations

Conflict of interest

There are no conflicts of interest to declare.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ranjineh Khojasteh, A., Toshani, H. Design nonlinear feedback strategy using H2/H∞ control and neural network based estimator for variable speed wind turbine. Int. J. Dynam. Control 10, 447–461 (2022). https://doi.org/10.1007/s40435-021-00813-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40435-021-00813-4

Keywords

Search

Navigation