Robust Control Strategy for a WECS Based at PMSG

Conference paper

Abstract

The wind energy boom in the world began in 1980’s. This work presents the modeling and control of a Wind Energy Conversion Systems (WECS) based Permanent Magnet Synchronous Generator (PMSG). The WECS adopts a back-to-back converter system with Voltage Source Inverter (VSI). In the strategy, the generator-side converter is used to tracks the maximum power point and the grid-side converter is responsible for the control of power flow and control the dc-link voltage. The control scheme uses a B-spline artificial neural network for tuning controllers when the system is subjected to disturbances. The currents from VSI’s are controlled in a synchronous orthogonal dq frame using an adaptive PI control. The B-spline neural network must be able to enhance the system performance and the online parameters updated can be possible. This work proposes the use of adaptive PI controllers to regulate the current and DC link voltage. The simulations results confirm that the proposed algorithm is remarkably faster and more efficient than the conventional PI. Comprehensive models of wind speed, wind turbine, PMSG and power electronic converters along with their control schemes are implemented in MATLAB/SIMULINK environment.

Keywords

Current control Neural network Permanent magnet synchronous generator Phase locked loop Voltage source inverter Wind power generation system 

References

  1. 1.
    O. Anaya-Lara, N. Jenkins, J. Ekanayake, P. Cartwright, M. Hughes, Wind Energy Generation Modelling and Control (Wiley, Chichester, 2009), pp. 110–112Google Scholar
  2. 2.
    T. Ackermann, Wind Power in Power Systems (Wiley, Chichester, 2005)CrossRefGoogle Scholar
  3. 3.
    B. Wu, Y. Lang, S. Kouro, Power Conversion and Control of Wind Energy Systems (IEEE Press, New York, 2011)CrossRefGoogle Scholar
  4. 4.
    Variable Boldea, Speed Generators (CRC Press, Boca Raton, 2006)Google Scholar
  5. 5.
    G. Abad, J. López, M.A. Rodríguez, L. Marroyo, G. Iwanski, Doubly Fed Induction Machine (Wiley, New Jersey, 2011), pp. 1–25CrossRefGoogle Scholar
  6. 6.
    M. Hoa, D. Subbaram Naidu., Advanced Control Strategies for Wind Energy Systems: An Overview, in International Conference on Power Systems, 2011, Vol. 1, 2011, pp. 1–8Google Scholar
  7. 7.
    X. Yao, X. Su, L. Tian, Wind turbine control strategy at lower wind velocity based on neural network PID control, in Intelligent Systems & Applications, May 2009, pp. 1–5Google Scholar
  8. 8.
    Z. Xing, Q. Li, X. Su, H. Guo, Application of BP neural network for wind turbines. Intell. Comp. Technol. Autom. 1, 42–44 (2009)Google Scholar
  9. 9.
    X. Yao, X. Su, L. Tian, Pitch angle control of variable pitch wind turbines based on NN PID, in 4th IEEE Conference on Industrial Electronics & Applications, May 2009, pp. 3235–3239Google Scholar
  10. 10.
    O. Aguilar, M. Saucedo Jose, R. Tapia, “On-line control strategy for a WECS with permanent magnet synchronous generator”, Lecture Notes in Engineering and Computer Science, in Proceedings of The World Congress on Engineering and Computer Science 2013, WCECS 2013, San Francisco, USA, 23–25 Oct. 2013, pp. 355–360 (2013)Google Scholar
  11. 11.
    A. Luna, J. Rocabert, G. Vazquez, P. Rodríguez, R. Teodorescu and F. Corcoles, Grid synchronization for advanced power processing and FACTS in wind power systems, in IEEE Industrial Electronics Conference, pp. 2915–2920Google Scholar
  12. 12.
    A. Valderrabano, J.M. Ramirez, Details on the implementation of a conventional StatCom’s control, in International Conference IEEE. Transmission and Distribution: Latin America, Bogota, Colombia, 2008Google Scholar
  13. 13.
    I. Munteanu, A.I. Bratcu, N.A. Cutululis, E. Ceanga, Optimal Control of Wind Energy Systems. (London, Springer, 2008) Google Scholar
  14. 14.
    E. Haque, M. Negnevitsky, K.M. Muttaqi, A novel control strategy for a variable-speed wind turbine with a permanent-magnet synchronous generator. IEEE Trans. Ind. Appl. 46(1), 331–339 (2010)CrossRefGoogle Scholar
  15. 15.
    L. Shuhui, T.A. Haskew, R.P. Swatloski, W. Gathings, Optimal and direct-current vector control of direct-driven PMSG wind turbines. IEEE Trans. Power Electron. 27(5), 2325–2337 (2012)CrossRefGoogle Scholar
  16. 16.
    J. Arrillaga, Y. Liu, N. Watson, N. Murray, Self-Commutating Converters for High Power Applications (Wiley, Chichester, 2009)CrossRefGoogle Scholar
  17. 17.
    J.C. Rosas, Simple Topologies for Power Conditioners and FACT’s Controllers, Ph.D. dissertation, CINVESTAV, Gdl, 2009Google Scholar
  18. 18.
    A. Yazdani, R. Iravani, Voltage-Sourced Converters in Power Systems Modeling, Control, and Applications. (Wiley, New Jersey, 2010)Google Scholar
  19. 19.
    J. Brown, C. Harris, Neurofuzzy Adaptive Modeling and Control. (Prentice Hall International, London, 1994)Google Scholar
  20. 20.
    D. Saad, On-line learning in neural networks. (Cambridge University Press, Cambridge, 1998)Google Scholar
  21. 21.
    J.M. Ramirez, R.E. Correa-Gutierrez, N.J. Castrillon-Gutierrez, A study on multiband PSS coordination. Int. J. Emerg. Elect. Power Syst. 10, 1–20 (2009) Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  1. 1.Engineering Department at PolytechnicUniversity of TulancingoTulancingoMexico

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