Identification of Coherent Generator Groups Based on Stochastic Subspace Algorithm

  • Wei Xin
  • Yingyun Sun
  • Wei Wang
  • Ting Yu
  • Haotian Li
  • Hanzhi Zhang
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 238)

Abstract

With continuous expansion of power grid scale, the research of low-frequency oscillation has been the hot spot of power system stable operation. The load of power system possesses the characteristic of continuous variation in certain time span. This variation usually is random, and its effect can be simulated by the influence of ambient noise on system. The conventional analysis method, getting state matrix from system mathematic model, has encountered application limitation. Therefore, under the premise of ambient noise, this chapter adopted stochastic subspace method to identify coherence generator group which is in low-frequency oscillation. Based on state-space model and measured signal, by identifying system state matrix through stochastic subspace method and analyzing its eigenvalue, the method of identifying system coherence generators could be found. Through analyzing the system example of 3 generators and 9 nodes, and 16 generators and 68 nodes, the result indicated that the identification method this chapter adopted had higher identification accuracy and calculation efficiency.

Keywords

Covariance Coherence 

Notes

Acknowledgments

This chapter is supported by National Natural Science Foundation Project (50907034) and the Fundamental Research Funds for the Central Universities (10MG08).

References

  1. 1.
    Pierre JW, Trudnowski DJ, Donnelly MK (1997) Initial results in electromechanical mode identification from ambient data. IEEE Trans Power Syst 12:1245–1251CrossRefGoogle Scholar
  2. 2.
    Sauer PW, Pai MA (1998) Power system dynamics and stability. Prentice Hall, New YorkGoogle Scholar
  3. 3.
    Wies RW (1999) Estimating low-frequency electromechanical modes of power systems using ambient data. Ph.D. Dissertation, University of Wyoming, Laramie, WYGoogle Scholar
  4. 4.
    Bergen AR, Vittal V (1999) Power systems analysis, 2nd edn. Prentice Hall, Upper Saddle River, NJGoogle Scholar
  5. 5.
    Van OP, De MB (1991) Subspace algorithms for the stochastic identification problem. In: The IEEE conference on decision and control, Brighton, UKGoogle Scholar
  6. 6.
    Peeters B, De RG (1999) Reference-based stochastic subspace identification for output only modal analysis. Mech Syst Signal Proc 13(6):855–878Google Scholar
  7. 7.
    Yuan RX, Jiang BF, Zhao SH (2011) Stochastic subspace identification for power system low frequency oscillations analysis. In: Proceedings of the Chinese Society of Universities for Electric Power System and its Automation, vol 23, issue 4, pp 51–55Google Scholar
  8. 8.
    Peter VO, Bart DM (1996) Subspace identification for linear systems. Katholieke Universities Leuven, pp 1–22Google Scholar
  9. 9.
    Graham R (2000) Power system oscillations. Kluwer Academic, London, pp 107–115Google Scholar
  10. 10.
    Zhou N, Trudnowski D, Pierre JW et al (2008) An algorithm for removing trends from power-system oscillation data. In: IEEE PES general meeting, Pittsburgh, PA, USA, pp 1–7Google Scholar
  11. 11.
    Anderson PM, Found AA (1977) Power system control and stability, vol I. The Iowa State University Press, Iowa, IAGoogle Scholar
  12. 12.
    Lardies J (1998) State space identification of vibrating system from multi output measurements[J]. Mech Syst Signal Proc 12(4): 543–558CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Wei Xin
    • 1
  • Yingyun Sun
    • 1
  • Wei Wang
    • 2
  • Ting Yu
    • 2
  • Haotian Li
    • 1
  • Hanzhi Zhang
    • 1
  1. 1.State Key Laboratory of Alternate Electrical Power System with Renewable Energy SourcesNorth China Electric Power UniversityBeijingChina
  2. 2.China Electric Power Research InstituteBeijingChina

Personalised recommendations