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access icon free Regional pole placement of wind turbine generator system via a Markovian approach

  • Author(s): Zhongwei Lin 1 ; Jizhen Liu 1 ; Weihai Zhang 2 ; Yuguang Niu 1
    • View affiliations
    • Affiliations: 1: State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, People's Republic of China ;
      2: College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266510, People's Republic of China
  • Source: Volume 10, Issue 15, 10 October 2016, p. 1771 – 1781
    DOI:  10.1049/iet-cta.2015.0983 , Print ISSN 1751-8644, Online ISSN 1751-8652
© The Institution of Engineering and Technology
Received 20/10/2015, Accepted 03/05/2016, Revised 04/04/2016, Published 13/06/2016

The wind turbine generator system (WTGS) is usually linearised around operating points, and traditional control techniques concentrate on local dynamic response nearby each operating point. However, the actual wind speed is switching frequently between different operating points, which could cause a serious negative influence on the stability and dynamic response of WTGS. To overcome this disadvantage, this study proposes a systematic method to combine the switching rule into the control design to improve the dynamic response of the mechanical side of WTGS. Through modelling the WTGS driven by the switching wind speed into a class of linearised Markovian jump controlled systems, the regional pole placement technique is developed for such a class of systems and applied on improving the dynamic response of WTGS. Combined with the H ∞ control, the proposed method can achieve better wind energy conversion efficiency and reduce the power volatility efficiently. Through combining the analysis on the actual historical wind speed into the control design, simulation results for a 2 MW wind turbine show the effectiveness of the proposed method.

Inspec keywords: Markov processes; wind turbines; control system synthesis; H∞ control; power generation control

Other keywords: dynamic response; linearised Markovian jump controlled systems; regional pole placement; wind turbine generator system; Markovian approach; control techniques; H∞ control; power 2 MW; local dynamic response; switching rule; regional pole placement technique; WTGS

Subjects: Markov processes; Control of electric power systems; Wind power plants; Optimal control; Markov processes; Control system analysis and synthesis methods

References

    1. 1)
      • 7. Yin, S., Wang, G., Karimi, H.R.: ‘Data-driven design of robust fault detection system for wind turbines’, Mechatronics, 2014, 24, (4), pp. 298–306.
    2. 2)
      • 9. Bakka, T., Karimi, H.R.: ‘H∞ static output-feedback control design with constrained information for offshore wind turbine system’, 2013, 350, (8), pp. 2244–2260.
    3. 3)
      • 6. Bianchi, F.D., Battista, H.D., Mantz, R.J.: ‘Wind turbine control systems principles, modelling and gain scheduling design’ (Springer, Netherlands, 2007).
    4. 4)
      • 4. Mathew, S., Philip, G.S.: ‘Advances in wind energy conversion technology’ (Springer, Berlin, 2011).
    5. 5)
      • 14. Shi, P., Li, F.: ‘A survey on Markovian jump systems: modeling and design’, Int. J. Control Autom. Syst., 2015, 13, pp. 1–16.
    6. 6)
      • 19. Florin, I., Anca, D.H., Poul, S., et al‘Wind turbine blockset in Matlab/Simulink. General overview and description of the model’. Aalborg University: Institut for Engergiteknik, Aalborg Universitet, 2004.
    7. 7)
      • 1. ‘GWEC: Global wind report’. 2014, http://www.gwec.net/GWECGlobalWind2014ReportLR.pdf.
    8. 8)
      • 8. Si, Y.L., Karimi, H.R., Gao, H.J.: ‘Modelling and optimization of a passive structural control design for a spar-type floating wind turbine’, Eng. Struct., 2014, 69, (15), pp. 168–182.
    9. 9)
      • 2. Muyeen, S.M.: ‘Wind energy conversion systems: technology and trends’ (Springer, Berlin, 2012).
    10. 10)
      • 15. Lin, Z.W., Lin, Y., Zhang, W.H.: ‘A unified design for state and output feedback H∞ control of nonlinear stochastic Markovian jump systems with state and disturbance-dependent noise’, Automatica, 2009, 41, pp. 2955–2962.
    11. 11)
      • 12. Sahin, A.D., Sen, Z.: ‘First-order Markov chain approach to wind speed modelling’, J. Wind Eng. Ind. Aerodyn., 2001, 89, pp. 263–269.
    12. 12)
      • 5. Houpis, G.S.: ‘Wind energy systems: control engineering design’ (CRC Press, 2012).
    13. 13)
      • 11. Nfaoui, H., Essiarab, H., Sayigh, A.A.M.: ‘A stochastic Markov chain model for simulating wind speed time series at Tangiers, Morocco’, Renew. Energy, 2004, 29, pp. 1407–1418.
    14. 14)
      • 18. Shi, P., Shue, S.P.: ‘Robust H∞ control for linear discrete-time systems with norm-bounded nonlinear uncertainties’, IEEE Trans. Autom. Control, 1999, 44, pp. 108–111.
    15. 15)
      • 20. Kuo, B.C. : ‘Automatic control systems’ (Prentice-Hall, Englewood Cliffs, NJ, 1982).
    16. 16)
      • 17. Nelson, B.L.: ‘Stochstic modeling (analysis & simulation)’ (MacGraw-Hill, New York, 1995).
    17. 17)
      • 13. Mariton, M.: ‘Jump linear systems in automatic control’ (Marcel Dekker, 1990).
    18. 18)
      • 3. Jha, A.R.: ‘Wind turbine technology’ (CRC Press, Florida, 2011).
    19. 19)
      • 16. Chilali, M., Gahinet, P.: ‘H∞ design with pole placement constraints: an LMI approach’, IEEE Trans. Autom. Control, 1996, 41, pp. 358–367.
    20. 20)
      • 10. Bakka, T., Karimi, H.R., Christiansen, S.: ‘Linear parameter-varying modelling and control of an offshore wind turbine with constrained information’, IET Control Theory Appl., 2014, 8, (1), pp. 22–29.
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