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Stability analysis of power system with multiple operating conditions considering the stochastic characteristic of wind speed

Stability analysis of power system with multiple operating conditions considering the stochastic characteristic of wind speed

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A new method to analyze the stability of power system with multiple operating conditions considering the stochastic characteristic of wind speed is proposed in this study. First, the parameter Weibull model is used to do fitting prediction of the wind speed variation trend in the short term. Second, the wind speed is divided into different intervals, and the probability density matrix of the conditional featured wind speed in each interval is calculated. Then, the system operating conditions are determined according to the conditional featured wind speed. On this basis, the continuous Markov power system model with multiple operating conditions considering the stochastic characteristic of wind speed is established, and the Lyapunov functional containing the continuous Markov power system model is constructed. Then, by applying the Dynkin Lemma to the weak infinitesimal operators of the functional, the robust stochastic stability linear matrix inequality (LMI) which satisfies the disturbance attenuation degree is derived. Finally, transform the robust stochastic stability LMI to the feasibility problem, so that the stability of power system could be identified. Time-domain simulation tests verify that the proposed method could identify the stability of power system with multiple operating conditions quickly and efficiently.

References

    1. 1)
    2. 2)
    3. 3)
    4. 4)
      • 4. Ma, J., Wang, T., Wang, Z.: ‘Interval eigenvalue analysis of closed-loop control for power system oscillation with interval parameters’. 2012 IEEE Power and Energy Society General Meeting, 2012, pp. 16.
    5. 5)
    6. 6)
      • 6. Vittal, E., O'Malley, M., Keane, A.: ‘A small-signal stability analysis of DFIG wind generation’. 8th Int. Workshop on Large-scale Integration of Wind Power into Power Systems, Breme, Germany, 14–15 October 2009.
    7. 7)
    8. 8)
      • 8. Sanchez-Gasca, J.J., Miller, N.W., Price, W.W.: ‘A modal analysis of a two-area system with significant wind power penetration’. Proc. IEEE Power Engineering Society Power System Conf. Expo., 2004, vol. 2, pp. 11481152.
    9. 9)
    10. 10)
      • 10. Pang, C.K., Dong, Z.Y., Zhang, P., et al: ‘Probabilistic analysis of power system small signal stability region’. Proc. Int. Conf. Control and Automation, June 2005.
    11. 11)
    12. 12)
      • 12. Xu, Z., Dong, Z.Y., Zhang, P.: ‘Probabilistic small signal analysis using Monte Carlo simulation’. Proc. IEEE Power Engineering Society General Meeting, 2005.
    13. 13)
      • 13. Wang, C., Shi, L., Yao, L., et al: ‘Modelling analysis in power system small signal stability considering uncertainty of wind generation’. Proc. IEEE Power and Energy Society General Meeting, Minneapolis, MN, USA, 2010, pp. 17.
    14. 14)
    15. 15)
    16. 16)
    17. 17)
      • 17. Zhu, H., Xu, J., Wang, X.: ‘The output forecast research of the wind farm based on GM-Weibull wind speed distribution combined model’. 4th IEEE Conf. on Industrial Electronics and Applications, ICIEA, 2009, pp. 21372140.
    18. 18)
      • 18. Goudarzi, A., Davidson, I.E., Ahmadi, A., et al: ‘Intelligent analysis of wind turbine power curve models’. 2014 IEEE Symp. on Computational Intelligence Applications in Smart Grid (CIASG), 2014, pp. 17.
    19. 19)
      • 19. Mathew, S.: ‘Wind energy: fundamentals, resource analysis and economics’ (Springer, Berlin, 2006).
    20. 20)
      • 20. He, Y., Zhao, L., Yan, M.: ‘Robust sliding mode control for a class of uncertain hybrid linear systems with Markovian jump parameter’. 2nd Int. Conf. on Education Technology and Computer, Shanghai, China, 22–24 June 2010.
    21. 21)
      • 21. Kundur, P.: ‘Power system stability and control’ (McGraw-Hill. Inc., New York, 1994).
    22. 22)
    23. 23)
    24. 24)
    25. 25)
    26. 26)
      • 26. Mariton, M.: ‘Jump linear systems in automatic control’ (Marcel Dekker, New York, 1990).
    27. 27)
      • 27. Bouks, E.K.: ‘Stochastic switching systems: analysis and design’ (Spring, Boston, 2005).
    28. 28)
      • 28. Yih-Huei, W., Erik, E., Kirsten, O.: ‘Development of an equivalent wind plant power-curve’. American Wind Energy Association Global Wind Power Conf., Dallas, Texas, USA, 23–26 May 2010.
    29. 29)
      • 29. Rogers, G.: ‘Power system oscillations’ (Kluwer, MA, 2000).
    30. 30)
    31. 31)
    32. 32)
      • 32. Kosterev, D., Meklin, A.: ‘Load modeling in WECC’. Proc. of 2006 IEEE Power System Conf. and Exposition, Atlanta, pp. 576581.
    33. 33)
      • 33. Kosterev, D., Meklin, A., Undrill, J., et al: ‘Load modeling in power system studies: WECC Progress Update’. Proc. IEEE PES General Meeting Conversion and Delivery of Electric Energy in the 21st Century, 2008.
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