access icon free Fatigue damage estimation and data-based control for wind turbines

The focus of this work is on fatigue estimation and data-based controller design for wind turbines. The main purpose is to include a model of the fatigue damage of the wind turbine components in the controller design and synthesis process. This study addresses an online fatigue estimation method based on hysteresis operators, which can be used in control loops. The authors propose a data-based model predictive control (MPC) strategy that incorporates an online fatigue estimation method through the objective function, where the ultimate goal in mind is to reduce the fatigue damage of the wind turbine components. The outcome is an adaptive or self-tuning MPC strategy for wind turbine fatigue damage reduction, which relies on parameter identification on previous measurement data. The results of the proposed strategy are compared with a baseline model predictive controller.

Inspec keywords: fatigue; wind turbines; predictive control; parameter estimation; machine control

Other keywords: data-based controller design; self-tuning MPC strategy; baseline model predictive controller; control loops; online fatigue estimation; wind turbine components; parameter identification; fatigue damage estimation; data-based model predictive control; hysteresis operators; wind turbine fatigue damage reduction

Subjects: Fracture mechanics and hardness (mechanical engineering); Control technology and theory (production); Power and plant engineering (mechanical engineering); Control of electric power systems; Simulation, modelling and identification; Fluid mechanics and aerodynamics (mechanical engineering); Optimal control

References

    1. 1)
      • 23. Yin, S., Yang, X., Karimi, H.R.: ‘Data-driven adaptive observer for fault diagnosis’, Math. Probl. Eng., 2012, 2012.
    2. 2)
    3. 3)
    4. 4)
    5. 5)
      • 39. Jonkman, J.M., Butterfield, S., Musial, W., Scott, G.: ‘Definition of a 5-MW reference wind turbine for offshore system development’. National Renewable Energy Laboratory Colorado, 2009.
    6. 6)
    7. 7)
      • 42. Ljung, L.: ‘System identification: theory for the user’ (Prentice-Hall, 1987).
    8. 8)
      • 2. Wöhler, A.: ‘Versuche über die Festigkeit der Eisenbahnwagenachsen’, Z. für Bauwesen, 1860, 10, pp. 160161.
    9. 9)
      • 7. Mirzaei, M., Soltani, M., Poulsen, N.K., Niemann, H.H.: ‘Model predictive control of wind turbines using uncertain LIDAR measurements’. Proc. American Control Conf. (ACC), 2013, pp. 22352240.
    10. 10)
      • 25. Johannesson, P.: ‘Rainflow analysis of switching Markov loads’. PhD thesis, Lund University, 1999.
    11. 11)
    12. 12)
      • 33. Mayergoyz, I.D.: ‘Mathematical models of hysteresis’ (Springer Verlag, 1991).
    13. 13)
    14. 14)
    15. 15)
    16. 16)
    17. 17)
    18. 18)
      • 32. Krasnosel'skiıˇ, M.A., Pokrovskiıˇ, A.V.: ‘Systems with hysteresis’ (Springer Verlag, 1989).
    19. 19)
    20. 20)
      • 6. Soltani, M., Wisniewski, R., Brath, P., Boyd, S.: ‘Load reduction of wind turbines using receding horizon control’. Proc. IEEE Conf. Control Applications (CCA), 2011, pp. 852857.
    21. 21)
      • 40. Maciejowski, J.M., Huzmezan, M.: ‘Predictive control with constraints’ (Springer, 1997).
    22. 22)
      • 44. Buhl, M.L.: ‘MCrunch user's guide for vrsion 1.00’, National Renewable Energy Laboratory, 2008.
    23. 23)
    24. 24)
      • 5. Endo, T., Mitsunaga, K., Nakagawa, H.: ‘Fatigue of metals subjected to varying stress-prediction of fatigue lives’. Preliminary Proc. of the Chugoku-Shikoku District Meeting, 1967, pp. 4144.
    25. 25)
      • 3. Palmgren, A.: ‘Die Lebensdauer von Kugellagern’, Z. des Vereins Deutscher Ingenieure, 1924, 68, (14), pp. 339341.
    26. 26)
      • 13. Brokate, M., Dreßler, K., Krejčí, P.: ‘Rainflow counting and energy dissipation for hysteresis models in elastoplasticity’, Eur. J. Mech. A, Solids, 1996, 15, (4), pp. 705737.
    27. 27)
      • 14. Brokate, M.: ‘Optimale Steuerung von gewöhnlichen Differentialgleichungen mit Nichtlinearitäten vom Hysteresis-Typ’, (P. Lang, 1987), vol. 35.
    28. 28)
      • 30. Bishop, N.W.M.: ‘Vibration fatigue analysis in the finite element environment’. XVI Encuentro Del Grupo Español De Fractura, Spain, 1999.
    29. 29)
    30. 30)
    31. 31)
      • 34. Brokate, M., Sprekels, J.: ‘Hysteresis and phase transitions’ (Springer Verlag, Applied Mathematical Sciences, 1996), vol. 121.
    32. 32)
    33. 33)
    34. 34)
    35. 35)
      • 41. Camacho, E.F., Bordons, C.: ‘Model predictive control’, (Springer, London, 2004).
    36. 36)
      • 4. Miner, M.A.: ‘Cumulative damage in fatigue’, J. Appl. Mech., 1945, 12, (3), pp. 159164.
    37. 37)
    38. 38)
      • 43. Grant, M., Boyd, S.: ‘CVX: Matlab software for disciplined convex programming, version 2.1’, http://www.cvxr.com/cvx, 2014.
    39. 39)
    40. 40)
    41. 41)
    42. 42)
      • 38. Chen, X.: ‘Control for unknown linear systems preceded by hysteresis Represented by Preisach model’. Proc. IEEE Conf. Decision and Control (CDC), 2013, pp. 66646669.
    43. 43)
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-cta.2014.0730
Loading

Related content

content/journals/10.1049/iet-cta.2014.0730
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading