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.
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References
Pierre JW, Trudnowski DJ, Donnelly MK (1997) Initial results in electromechanical mode identification from ambient data. IEEE Trans Power Syst 12:1245–1251
Sauer PW, Pai MA (1998) Power system dynamics and stability. Prentice Hall, New York
Wies RW (1999) Estimating low-frequency electromechanical modes of power systems using ambient data. Ph.D. Dissertation, University of Wyoming, Laramie, WY
Bergen AR, Vittal V (1999) Power systems analysis, 2nd edn. Prentice Hall, Upper Saddle River, NJ
Van OP, De MB (1991) Subspace algorithms for the stochastic identification problem. In: The IEEE conference on decision and control, Brighton, UK
Peeters B, De RG (1999) Reference-based stochastic subspace identification for output only modal analysis. Mech Syst Signal Proc 13(6):855–878
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 51–55
Peter VO, Bart DM (1996) Subspace identification for linear systems. Katholieke Universities Leuven, pp 1–22
Graham R (2000) Power system oscillations. Kluwer Academic, London, pp 107–115
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 1–7
Anderson PM, Found AA (1977) Power system control and stability, vol I. The Iowa State University Press, Iowa, IA
Lardies J (1998) State space identification of vibrating system from multi output measurements[J]. Mech Syst Signal Proc 12(4): 543–558
Acknowledgments
This chapter is supported by National Natural Science Foundation Project (50907034) and the Fundamental Research Funds for the Central Universities (10MG08).
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Xin, W., Sun, Y., Wang, W., Yu, T., Li, H., Zhang, H. (2014). Identification of Coherent Generator Groups Based on Stochastic Subspace Algorithm. In: Xing, S., Chen, S., Wei, Z., Xia, J. (eds) Unifying Electrical Engineering and Electronics Engineering. Lecture Notes in Electrical Engineering, vol 238. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-4981-2_8
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DOI: https://doi.org/10.1007/978-1-4614-4981-2_8
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