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Frontiers of Mechanical Engineering

, Volume 12, Issue 3, pp 377–388 | Cite as

Power maximization of variable-speed variable-pitch wind turbines using passive adaptive neural fault tolerant control

  • Hamed Habibi
  • Hamed Rahimi Nohooji
  • Ian Howard
Research Article

Abstract

Power maximization has always been a practical consideration in wind turbines. The question of how to address optimal power capture, especially when the system dynamics are nonlinear and the actuators are subject to unknown faults, is significant. This paper studies the control methodology for variable-speed variable-pitch wind turbines including the effects of uncertain nonlinear dynamics, system fault uncertainties, and unknown external disturbances. The nonlinear model of the wind turbine is presented, and the problem of maximizing extracted energy is formulated by designing the optimal desired states. With the known system, a model-based nonlinear controller is designed; then, to handle uncertainties, the unknown nonlinearities of the wind turbine are estimated by utilizing radial basis function neural networks. The adaptive neural fault tolerant control is designed passively to be robust on model uncertainties, disturbances including wind speed and model noises, and completely unknown actuator faults including generator torque and pitch actuator torque. The Lyapunov direct method is employed to prove that the closed-loop system is uniformly bounded. Simulation studies are performed to verify the effectiveness of the proposed method.

Keywords

wind turbine nonlinear model maximum power tracking passive fault tolerant control adaptive neural control 

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References

  1. 1.
    Spudic V, Jelavic M, Baotic M. Supervisory controller for reduction of wind turbine loads in curtailed operation. Control Engineering Practice, 2015, 36: 72–86CrossRefGoogle Scholar
  2. 2.
    Bianchi F D, De Battista H, Mantz R J. Wind Turbine Control Systems: Principles, Modelling and Gain Scheduling Design. London: Springer Science & Business Media, 2006Google Scholar
  3. 3.
    Kamal E, Aitouche A, Abbes D. Robust fuzzy scheduler fault tolerant control of wind energy systems subject to sensor and actuator faults. International Journal of Electrical Power & Energy Systems, 2014, 55: 402–419CrossRefGoogle Scholar
  4. 4.
    Njiri J G, Söffker D. State-of-the-art in wind turbine control: Trends and challenges. Renewable & Sustainable Energy Reviews, 2016, 60: 377–393CrossRefGoogle Scholar
  5. 5.
    Yu X, Jiang J. A survey of fault-tolerant controllers based on safetyrelated issues. Annual Reviews in Control, 2015, 39: 46–57CrossRefGoogle Scholar
  6. 6.
    Kandukuri S T, Klausen A, Karimi H R, et al. A review of diagnostics and prognostics of low-speed machinery towards wind turbine farm-level health management. Renewable & Sustainable Energy Reviews, 2016, 53: 697–708CrossRefGoogle Scholar
  7. 7.
    Gao Z, Cecati C, Ding S X. A survey of fault diagnosis and faulttolerant techniques—Part I: Fault diagnosis with model-based and signal-based approaches. IEEE Transactions on Industrial Electronics, 2015, 62(6): 3757–3767CrossRefGoogle Scholar
  8. 8.
    Gao Z, Cecati C, Ding S X. A Survey of fault diagnosis and faulttolerant techniques—Part II: Fault diagnosis with knowledge-based and hybrid/active approaches. IEEE Transactions on Industrial Electronics, 2015, 62(6): 3768–3774Google Scholar
  9. 9.
    Odgaard P F, Stoustrup J. A benchmark evaluation of fault tolerant wind turbine control concepts. IEEE Transactions on Control Systems Technology, 2015, 23(3): 1221–1228CrossRefGoogle Scholar
  10. 10.
    Vidal Y, Tutivén C, Rodellar J, et al. Fault diagnosis and faulttolerant control of wind turbines via a discrete time controller with a disturbance compensator. Energies, 2015, 8(5): 4300–4316CrossRefGoogle Scholar
  11. 11.
    Blanke M, Kinnaert M, Lunze J, et al. Diagnosis and Fault-Tolerant Control. 2nd ed. New York: Springer, 2006zbMATHGoogle Scholar
  12. 12.
    Odgaard P F, Stoustrup J, Kinnaert M. Fault-tolerant control of wind turbines: A benchmark model. IEEE Transactions on Control Systems Technology, 2013, 21(4): 1168–1182CrossRefGoogle Scholar
  13. 13.
    Habibi H, Koma A Y, Sharifian A. Power and velocity control of wind turbines by adaptive fuzzy controller during full load operation. Iranian Journal of Fuzzy Systems, 2016, 13(3): 35–48MathSciNetGoogle Scholar
  14. 14.
    Sloth C, Esbensen T, Stoustrup J. Active and passive fault-tolerant LPV control of wind turbines. In: Proceedings of American Control Conference (ACC). 2010, 4640–4646Google Scholar
  15. 15.
    Esbensen T, Jensen B, Niss M, et al. Joint Power and Speed Control of Wind Turbines. Aalborg University, Project Report 08gr830. 2008Google Scholar
  16. 16.
    Johnson K E, Fingersh L J, Balas M J, et al. Methods for increasing Region 2 power capture on a variable-speed wind turbine. Journal of Solar Energy Engineering, 2004, 126(4): 1092–1100CrossRefGoogle Scholar
  17. 17.
    Iyasere E, Salah M, Dawson D, et al. Optimum seeking-based nonlinear controller to maximise energy capture in a variable speed wind turbine. IET Control Theory & Applications, 2012, 6(4): 526–532MathSciNetCrossRefGoogle Scholar
  18. 18.
    Boukhezzar B, Siguerdidjane H, Hand M M. Nonlinear control of variable-speed wind turbines for generator torque limiting and power optimization. Journal of Solar Energy Engineering, 2006, 128 (4): 516–530CrossRefGoogle Scholar
  19. 19.
    Østergaard K Z, Brath P, Stoustrup J. Estimation of effective wind speed. Journal of Physics: Conference Series, 2007, 75(1): 012082Google Scholar
  20. 20.
    Johnson K E, Pao L Y, Balas M J, et al. Control of variable-speed wind turbines: Standard and adaptive techniques for maximizing energy capture. IEEE Control Systems, 2006, 26(3): 70–81CrossRefGoogle Scholar
  21. 21.
    Li S, Wang H, Tian Y, et al. A RBF neural network based MPPT method for variable speed wind turbine system. IFAC-PapersOn-Line, 2015, 48(21): 244–250CrossRefGoogle Scholar
  22. 22.
    Odgaard P F, Stoustrup J. A benchmark evaluation of fault tolerant wind turbine control concepts. IEEE Transactions on Control Systems Technology, 2015, 23(3): 1221–1228CrossRefGoogle Scholar
  23. 23.
    Odgaard P F, Stoustrup J. An evaluation of fault tolerant wind turbine control schemes applied to a benchmark model. In: Proceedings of IEEE Conference on Control Applications (CCA). IEEE, 2014, 1366–1371Google Scholar
  24. 24.
    Odgaard P F, Stoustrup J, Nielsen R, et al. Observer based detection of sensor faults in wind turbines. In: Proceedings of European Wind Energy Conference. 2009, 4421–4430Google Scholar
  25. 25.
    Tabatabaeipour S M, Odgaard P F, Bak T, et al. Fault detection of wind turbines with uncertain parameters: A set-membership approach. Energies, 2012, 5(12): 2424–2448CrossRefGoogle Scholar
  26. 26.
    Badihi H, Zhang Y, Hong H. Fuzzy gain-scheduled active faulttolerant control of a wind turbine. Journal of the Franklin Institute, 2014, 351(7): 3677–3706CrossRefzbMATHGoogle Scholar
  27. 27.
    Sloth C, Esbensen T, Stoustrup J. Robust and fault-tolerant linear parameter-varying control of wind turbines. Mechatronics, 2011, 21 (4): 645–659CrossRefGoogle Scholar
  28. 28.
    Boukhezzar B, Siguerdidjane H. Comparison between linear and nonlinear control strategies for variable speed wind turbines. Control Engineering Practice, 2010, 18(12): 1357–1368CrossRefGoogle Scholar
  29. 29.
    Tang C, Guo Y, Jiang J. Nonlinear dual-mode control of variablespeed wind turbines with doubly fed induction generators. IEEE Transactions on Control Systems Technology, 2011, 19(4): 744–756CrossRefGoogle Scholar
  30. 30.
    Boukhezzar B, Siguerdidjane H. Nonlinear control with wind estimation of a DFIG variable speed wind turbine for power capture optimization. Energy Conversion and Management, 2009, 50(4): 885–892CrossRefGoogle Scholar
  31. 31.
    Civelek Z, Lüy M, Çam E, et al. Control of pitch angle of wind turbine by fuzzy PID controller. Intelligent Automation & Soft Computing, 2016, 22(3): 463–471MathSciNetCrossRefGoogle Scholar
  32. 32.
    Van T L, Nguyen T H, Lee D C. Advanced pitch angle control based on fuzzy logic for variable-speed wind turbine systems. IEEE Transactions on Energy Conversion, 2015, 30(2): 578–587CrossRefGoogle Scholar
  33. 33.
    Han B, Zhou L, Yang F, et al. Individual pitch controller based on fuzzy logic control for wind turbine load mitigation. IET Renewable Power Generation, 2016, 10(5): 687–693CrossRefGoogle Scholar
  34. 34.
    Medjber A, Guessoum A, Belmili H, et al. New neural network and fuzzy logic controllers to monitor maximum power for wind energy conversion system. Energy, 2016, 106: 137–146CrossRefGoogle Scholar
  35. 35.
    Assareh E, Biglari M. A novel approach to capture the maximum power from variable speed wind turbines using PI controller, RBF neural network and GSA evolutionary algorithm. Renewable & Sustainable Energy Reviews, 2015, 51: 1023–1037CrossRefGoogle Scholar
  36. 36.
    Heier S. Grid Integration ofWind Energy Conversion Systems. New York: John Wiley & Sons, Inc., 1998Google Scholar
  37. 37.
    Wang H, Pintea A, Christov N, et al. Modelling and recursive power control of horizontal variable speed wind turbines. Journal of Control Engineering and Applied Informatics, 2012, 14(4): 33–41Google Scholar
  38. 38.
    Hand M, Johnson K, Fingersh L, et al. Advanced Control Design and Field Testing for Wind Turbines at the National Renewable Energy Laboratory. National Renewable Energy Laboratory Report, NREL/CP-500-36118. 2004Google Scholar
  39. 39.
    Esbensen T, Sloth C. Fault diagnosis and fault-tolerant control of wind turbines. Dissertation for the Master’s Degree. Aalborg: Aalborg University, 2009Google Scholar
  40. 40.
    Hammerum K. A fatigue approach to wind turbine control. Technical University of Denmark, DK-2800 Kgs. Lyngby, 2006Google Scholar
  41. 41.
    Isermann R. Fault-Diagnosis Systems: An Introduction from Fault Detection to Fault Tolerance. New York: Springer Science & Business Media, 2006CrossRefGoogle Scholar
  42. 42.
    Kamal E, Aitouche A. Robust fault tolerant control of DFIG wind energy systems with unknown inputs. Renewable Energy, 2013, 56: 2–15CrossRefGoogle Scholar
  43. 43.
    Ge S S, Wang C. Adaptive neural control of uncertain MIMO nonlinear systems. IEEE Transactions on Neural Networks, 2004, 15(3): 674–692CrossRefGoogle Scholar
  44. 44.
    Yu H, Xie T, Paszczynski S, et al. Advantages of radial basis function networks for dynamic system design. IEEE Transactions on Industrial Electronics, 2011, 58(12): 5438–5450CrossRefGoogle Scholar
  45. 45.
    Liu J. Radial Basis Function (RBF) Neural Network Control for Mechanical Systems: Design, Analysis and Matlab Simulation. New York: Springer Science & Business Media, 2013CrossRefzbMATHGoogle Scholar
  46. 46.
    Polycarpou M M, Ioannou P A. A robust adaptive nonlinear control design. Automatica, 1996, 32(3): 423–427MathSciNetCrossRefzbMATHGoogle Scholar
  47. 47.
    Rahimi H N, Nazemizadeh M. Dynamic analysis and intelligent control techniques for flexible manipulators: A review. Advanced Robotics, 2013, 28(2): 63–76CrossRefGoogle Scholar
  48. 48.
    Slotine J J E, Li W. Applied Nonlinear Control. Englewood Cliffs: Prentice-Hall, 1991Google Scholar

Copyright information

© Higher Education Press and Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  • Hamed Habibi
    • 1
  • Hamed Rahimi Nohooji
    • 1
  • Ian Howard
    • 1
  1. 1.Faculty of Science and Engineering, School of Civil and Mechanical EngineeringCurtin UniversityPerthAustralia

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