Skip to main content

Adaptive Guaranteed Performance Control of Wind Energy Systems

  • Chapter
  • First Online:
Advanced Control and Optimization Paradigms for Wind Energy Systems

Part of the book series: Power Systems ((POWSYS))

Abstract

In this chapter, we present an adaptive guaranteed performance controller for wind energy conversion system (WECS) equipped with doubly fed induction generator (DFIG). The proposed controller consists of outer loop control concerning the aeroturbine mechanical subsystem, and inner loop control concerning the electrical subsystem. As opposed to most existing studies, we are capable of quantifying and further guaranteeing the system performance on both transient and steady state stages with the help of error transformation techniques. The stability is guaranteed via standard Lyapunov synthesis. Finally, the effectiveness of the proposed scheme is validated on a 1.5 MW DFIG-based wind turbine using the FAST (Fatigue, Aerodynamics, Structures, and Turbulence) simulator developed by the National Renewable Energy Laboratory (NREL).

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Bose B (2010) Global warming: energy, environmental pollution, and the impact of power electronics. IEEE Ind Electron Mag 4(1):6–17

    Article  Google Scholar 

  2. Kaldellis JK (2008) The wind potential impact on the maximum wind energy penetration in autonomous electrical grids. Renew Energy 33(7):1665–1677

    Article  Google Scholar 

  3. Joselin Herbert GM, Iniyan S, Sreevalsan E, Rajapandian S (2007) A review of wind energy technologies. Renew Sustain Energy Rev 11(6):1117–1145

    Article  Google Scholar 

  4. Todeschini G, Emanuel AE (2011) Transient response of a wind energy conversion system used as active filter. IEEE Trans Energy Convers 26(2):522–531

    Article  Google Scholar 

  5. Munteanu I, Cutululis NA, Bratcu AI, Ceanga E (2005) Optimization of variable speed wind power systems based on a LQG approach. Control Eng Pract 13(7):903–912

    Article  Google Scholar 

  6. Bououden S, Chadli M, Filali S, Hajjaji AE (2012) Fuzzy model based multivariable predictive control of a variable speed wind turbine: LMI approach. Renew Energy 37(1):434–439

    Article  Google Scholar 

  7. Boukhezzar B, Siguerdidjane H (2009) Nonlinear control with wind estimation of a DFIG variable speed wind turbine for power capture optimization. Energy Convers Manag 50(4):885–892

    Article  Google Scholar 

  8. Benbouzid M, Beltran B, Amirat Y, Yao G, Han J, Mangel H (2014) Second-order sliding mode control for DFIG-based wind turbines fault ride-through capability enhancement. ISA Trans 53(3):827–833

    Article  Google Scholar 

  9. Meng W, Yang Q, Ying Y, Sun Y, Yang Z, Sun Y (2013) Adaptive power capture control of variable-speed wind energy conversion systems with guaranteed transient and steady-state performance. IEEE Trans Energy Convers 28(3):716–725

    Article  Google Scholar 

  10. Meng W, Yang Q, Sun Y (2016) Guaranteed performance control of DFIG variable-speed wind turbines. IEEE Trans Control Syst Technol 24(6):2215–2223

    Article  Google Scholar 

  11. Beltran B, Ahmed-Ali T, Benbouzid M (2009) High-order sliding-mode control of variable-speed wind turbines. IEEE Trans Ind Electron 56(9):3314–3321

    Article  Google Scholar 

  12. Evangelista C, Valenciaga F, Puleston P (2013) Active and reactive power control for wind turbine based on a MIMO 2-sliding mode algorithm with variable gains. IEEE Trans Energy Convers 28(3):682–689

    Article  Google Scholar 

  13. Valenciaga F, Puleston PF, Spurgeon SK (2009) A geometric approach for the design of MIMO sliding controllers. Application to a wind-driven doubly fed induction generator. Int J Robust Nonlinear Control 19(1):22–39

    Article  MathSciNet  Google Scholar 

  14. Boukhezzar B, Lupu L, Siguerdidjane H, Hand M (2007) Multivariable control strategy for variable speed, variable pitch wind turbines. Renew Energy 32(8):1273–1287

    Article  Google Scholar 

  15. Kumar A, Stol K (2010) Simulating feedback linearization control of wind turbines using high-order models. Wind Energy 13(5):419–432

    Article  Google Scholar 

  16. Beltran B, El Hachemi Benbouzid M, Ahmed-Ali T (2012) Second-order sliding mode control of a doubly fed induction generator driven wind turbine. IEEE Trans Energy Convers 27(2):261–269

    Article  Google Scholar 

  17. Bechlioulis CP, Rovithakis GA (2008) Robust adaptive control of feedback linearizable mimo nonlinear systems with prescribed performance. IEEE Trans Autom Control 53(9):2090–2099

    Article  MathSciNet  Google Scholar 

  18. Slotine JJE, Li W et al (1991) Applied nonlinear control. Prentice-Hall Englewood Cliffs, NJ

    MATH  Google Scholar 

  19. Chen M, Ge SS, Ren B (2011) Adaptive tracking control of uncertain mimo nonlinear systems with input constraints. Automatica 47(3):452–465

    Article  MathSciNet  Google Scholar 

  20. Narendra K, Annaswamy A (1987) A new adaptive law for robust adaptation without persistent excitation. IEEE Trans Autom Control 32(2):134–145

    Article  MathSciNet  Google Scholar 

  21. Yang Q, Jagannathan S (2012) Reinforcement learning controller design for affine nonlinear discrete-time systems using online approximators. IEEE Trans Syst Man Cybern Part B Cybern 42(2):377–390

    Google Scholar 

  22. Bechlioulis CP, Rovithakis GA (2014) A low-complexity global approximation-free control scheme with prescribed performance for unknown pure feedback systems. Automatica 50(4):1217–1226

    Article  MathSciNet  Google Scholar 

  23. Lewis FL, Jagannathan S, Yesildirak A (1999) Neural network control of robot manipulators and non-linear systems. Taylor & Francis, Philadelphia, PA

    Google Scholar 

  24. National Renewable Energy Laboratory, Golden, CO. (2007, Feb). http://wind.nrel.gov/designcodes/simulators/fast/

  25. Beltran B, Ahmed-Ali T, El Hachemi Benbouzid M (2008) Sliding mode power control of variable-speed wind energy conversion systems. IEEE Trans Energy Convers 23(2):551–558

    Article  Google Scholar 

  26. Buhl Jr ML, Manjock A (2006) A comparison of wind turbine aeroelastic codes used for certification. Natl Renew Energy Lab, Golden, CO, NREL/CP-500-39113

    Google Scholar 

  27. Jonkman BJ, Buhl Jr ML, Turbsim user’s guide. Technical Report NREL/TP-500-41136. National Renewable Energy Laboratory (NREL), Golden, CO. http://wind.nrel.gov/designcodes/simulators/fast/

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Qinmin Yang .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Meng, W., Yang, Q. (2019). Adaptive Guaranteed Performance Control of Wind Energy Systems. In: Precup, RE., Kamal, T., Zulqadar Hassan, S. (eds) Advanced Control and Optimization Paradigms for Wind Energy Systems. Power Systems. Springer, Singapore. https://doi.org/10.1007/978-981-13-5995-8_6

Download citation

  • DOI: https://doi.org/10.1007/978-981-13-5995-8_6

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-5994-1

  • Online ISBN: 978-981-13-5995-8

  • eBook Packages: EnergyEnergy (R0)

Publish with us

Policies and ethics