Multibody System Dynamics

, Volume 16, Issue 3, pp 291–308 | Cite as

Aero-servo-elastic modeling and control of wind turbines using finite-element multibody procedures

  • Carlo L. Bottasso
  • Alessandro Croce
  • Barbara Savini
  • Walter Sirchi
  • Lorenzo Trainelli
Article

Abstract

In this paper, we report on our ongoing research in the area of aeroelastic modeling and control of wind turbine generators. At first, we describe a finite-element-based multibody dynamics code that is used in this effort for modeling wind turbine aeroelastic systems. Next, we formulate an adaptive nonlinear model-predictive controller. The adaptive element in the formulation enables the controller to correct the deficiencies of the reduced model used for the prediction, and to self-adjust to changing operating conditions. In this work, we verify the performance of the controller when the solution of the prediction problem is obtained by means of a direct transcription approach. The tests conducted on gust response and turbulent wind operations provide some benchmark results against which to compare the performance of a real-time neural controller currently under development.

Keywords

Multibody dynamics Model-predictive control Aeroelasticity Wind energy Neural networks 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Wind Turbine Generator System–Part 1: Safety Requirements, International Standard IEC 61400-1 (1999)Google Scholar
  2. 2.
    Barclay, A., Gill, P.E., Rosen, J.B.: SQP methods and their application to numerical optimal control. Report NA 97-3. Department of Mathematics, University of California, San Diego (1997)Google Scholar
  3. 3.
    Bauchau, O.A., Bottasso, C.L., Trainelli, L.: Robust integration schemes for flexible multibody systems. Comput. Methods Appl. Mech. Eng. 192, 395–420 (2003)Google Scholar
  4. 4.
    Bauchau, O.A., Bottasso, C.L., Nikishkov, Y.G.: Modeling rotorcraft dynamics with finite element multibody procedures. Math. Comput. Model. 33, 1113–1137 (2001)Google Scholar
  5. 5.
    Betts, J.T.: Practical Methods for Optimal Control Using Non-Linear Programming. SIAM, Philadelphia (2001)Google Scholar
  6. 6.
    Bottasso, C.L., Chang, C.-S., Croce, A., Leonello, D., Riviello, L.: Adaptive planning and tracking of trajectories for the simulation of maneuvers with multibody models, special issue on Computational Multibody Dynamics. Comput. Methods Appl. Mech. Eng. 195, 7052–7072 (2006)Google Scholar
  7. 7.
    Bottasso, C.L., Riviello, L.: Trimming of multibody rotorcraft models by a neural model-predictive auto-pilot. In: ECCOMAS Multibody Dynamics 2005, Madrid, Spain, June 21–24 (2005)Google Scholar
  8. 8.
    Fausett, L.: Fundamentals of Neural Networks. Prentice-Hall, New York (1994)Google Scholar
  9. 9.
    Findeisen, R., Imland, L., Allgöwer, F., Foss, B.A.: State and output feedback nonlinear model predictive control: an overview. Eur. J. Control 9, 190–206 (2003)Google Scholar
  10. 10.
    Hornik, K., Stinchombe, M., White, H.: Multi-layer feed-forward networks are universal approximators. Neural Netw. 2, 359–366 (1989)Google Scholar
  11. 11.
    Narendra, S., Parthasarathy, K.: Identification and control of dynamical systems using neural networks. IEEE Trans. Neural Netw. 1, 4–26 (1990)Google Scholar
  12. 12.
    Peters, D.A., He, C.J.: Finite state induced flow models–Part II: Three-dimensional rotor disk. J. Airc. 32, 323–333 (1995)Google Scholar
  13. 13.
    Pitt, D.M., Peters, D.A.: Theoretical prediction of dynamic inflow derivatives. Vertica 5, 21–34 (1981)Google Scholar
  14. 14.
    Powles, S.R.J.: The effects of tower shadow on the dynamics of a horizontal-axis wind turbine. Wind Eng. 7, 26–42 (1983)Google Scholar
  15. 15.
    Qin, S.J., Badgwell, T.A.: An overview of industrial model predictive control technology. In: Kantor, J.C., Garcia, C.E., Carnahan, B. (eds.) Chemical Process Control–AIChE Symposium Series. New York, pp. 232–256 (1997)Google Scholar
  16. 16.
    Qin, S.J., Badgwell, T.A.: An overview of nonlinear model predictive control applications. In: Allgöwer, F., Zheng, A. (eds.) Nonlinear Predictive Control. Basel, Birkhäuser, 369–393 (2000).Google Scholar
  17. 17.
    Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning internal representations by error propagation. In: Rumelhart, D.E., McClelland, J. (eds.) Parallel Data Processing, vol. 1, 318–362 (1986)Google Scholar

Copyright information

© Springer Science+Business Media, Inc. 2006

Authors and Affiliations

  • Carlo L. Bottasso
    • 1
  • Alessandro Croce
    • 1
  • Barbara Savini
    • 1
  • Walter Sirchi
    • 1
  • Lorenzo Trainelli
    • 1
  1. 1.Dipartimento di Ingegneria Aerospaziale, Politecnico di MilanoMilanoItaly

Personalised recommendations