Frontiers of Mechanical Engineering

, Volume 12, Issue 3, pp 367–376 | Cite as

Hierarchical parameter estimation of DFIG and drive train system in a wind turbine generator

  • Xueping Pan
  • Ping Ju
  • Feng Wu
  • Yuqing Jin
Research Article


A new hierarchical parameter estimation method for doubly fed induction generator (DFIG) and drive train system in a wind turbine generator (WTG) is proposed in this paper. Firstly, the parameters of the DFIG and the drive train are estimated locally under different types of disturbances. Secondly, a coordination estimation method is further applied to identify the parameters of the DFIG and the drive train simultaneously with the purpose of attaining the global optimal estimation results. The main benefit of the proposed scheme is the improved estimation accuracy. Estimation results confirm the applicability of the proposed estimation technique.


wind turbine generator DFIG drive train system hierarchical parameter estimation method trajectory sensitivity technique 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.



This research was supported by the National Natural Science Foundation of China (Major Program) (Grant Nos. 51190102 and 51207045).


  1. 1.
    Rose J, Hiskens I A. Estimating wind turbine parameters and qualifying their effects on dynamic behavior. In: Proceedings of 2008 IEEE Power and Energy Society General Meeting Conversion and Delivery of Electrical Energy in the 21st Century. Pennsylvania, 2008, 1–7Google Scholar
  2. 2.
    Pan X, Ju P, Xu Q, et al. A two-step method for estimating DFIG parameters in a wind turbine and the measurement selection. Proceedings of the CSEE, 2013, 33(13): 116–126 (in Chinese)Google Scholar
  3. 3.
    IEEE Guide: Test Procedures for Synchronous Machines. IEEE Standard 115-2009, 2010Google Scholar
  4. 4.
    IEEE Guide: Identification, Testing, and Evaluation of the Dynamic Performance of Excitation Control Systems. IEEE Standard 421.2- 1990, 1990Google Scholar
  5. 5.
    Asmine M, Brochu J, Fortmann J, et al. Model validation for wind turbine generator models. IEEE Transactions on Power Systems, 2011, 26(3): 1769–1782CrossRefGoogle Scholar
  6. 6.
    Marvik J I, Endegnanew A G. Wind turbine model validation with measure. Energy Procedia, 2012, 24: 143–150CrossRefGoogle Scholar
  7. 7.
    Jiménez F, Vigueras-Rodriguez A, Gómez-Lázaro E, et al. Validation of a mechanical model for fault ride-through: Application to a Gamesa G52 commercial wind turbine. IEEE Transactions on Energy Conversion, 2013, 28(3): 707–715CrossRefGoogle Scholar
  8. 8.
    Jiménez F, Gómez-Lázaro E, Fuentes J A, et al. Validation of a double fed induction generator wind turbine model and wind farm verification following the Spanish grid code. Wind Energy (Chichester, England), 2012, 15(4): 645–659CrossRefGoogle Scholar
  9. 9.
    Jiménez F, Gómez-Lázaro E, Fuentes J A, et al. Validation of a DFIG wind turbine model submitted to two-phase voltage dips following the Spanish grid code. Renewable Energy, 2013, 57: 27–34CrossRefGoogle Scholar
  10. 10.
    Trilla L, Gomis-Bellmunt O, Junyent-Ferré A, et al. Modeling and validation of DFIG 3-MW wind turbine using field test data of balanced and unbalanced voltage sags. IEEE Transactions on Power Systems, 2011, 2(4): 509–519Google Scholar
  11. 11.
    Brochu J, Larose C, Gagnon R. Validation of single- and multiplemachine equivalents for modeling wind power plants. IEEE Transactions on Energy Conversion, 2010, 26(2): 532–541CrossRefGoogle Scholar
  12. 12.
    Pedersen J K, Helgelsen-Pedersen K O, Kjølstad Poulsen N, et al. Contribution to a dynamic wind turbine model validation from a wind farm islanding experiment. Electric Power Systems Research, 2003, 64(1): 41–51CrossRefGoogle Scholar
  13. 13.
    van der Veen G J, van Wingerden J W, Fleming P A, et al. Global data-driven modelling of wind turbines in the presence of turbulence. Control Engineering Practice, 2013, 21(4): 441–454CrossRefGoogle Scholar
  14. 14.
    Kennedy J M, Fox B, Littler T, et al. Validation of fixed speed induction generator models for inertial response using wind farm measurements. IEEE Transactions on Power Systems, 2011, 26(3): 1454–1461CrossRefGoogle Scholar
  15. 15.
    González-Longatt F, Regulski P, Wall P, et al. Fixed speed wind generator model parameter estimation using improved particle swarm optimization and system frequency disturbances. In: Proceedings of IET Conference on Renewable Power Generation. Edinburgh: IEEE, 2011, 1–6Google Scholar
  16. 16.
    Bekker J C, Vermeulen H J. Parameter estimation of a doubly-fed induction generator in a wind generation topology. In: Proceedings of 47th International Universities Power Engineering Conference (UPEC). London: IEEE, 2012, 1–6Google Scholar
  17. 17.
    Wu F, Zhang X, Godfrey K, et al. Small signal stability analysis and optimal control of a wind turbine with doubly fed induction generator. IET Generation, Transmission & Distribution, 2007, 1(5): 751–760CrossRefGoogle Scholar
  18. 18.
    Mei F, Pal B C. Modal analysis of grid-connected doubly-fed induction generators. IEEE Transactions on Energy Conversion, 2007, 22(3): 728–736CrossRefGoogle Scholar
  19. 19.
    Ju P, Wu F, Jin Y, et al. Modelling and Control of Renewable Power Generation System. Beijing: Science Press, 2014 (in Chinese)Google Scholar
  20. 20.
    Trelea I. The particle swarm optimization algorithm: Convergence analysis and parameter selection. Information Processing Letters, 2003, 85(6): 317–325MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Higher Education Press and Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  1. 1.College of Energy and Electrical EngineeringHohai UniversityNanjingChina

Personalised recommendations