Low-Frequency Oscillations Identification in Interconnected Power System Using PMU


Article Preview

This paper presents the result of identification of low-frequency oscillations in 9-bus test model of power system. The identification is achieved by novel technique developed by authors. The technique is described in detail in [1. It is based on treating synchronized phasor measurements. Applicability of identified state matrix for electromechanical oscillations monitoring is demonstrated in the paper.



Advanced Materials Research (Volumes 860-863)

Main Theme:

Edited by:

Qunjie Xu, Yongguang Li and Xiu Yang




P. V. Chusovitin et al., "Low-Frequency Oscillations Identification in Interconnected Power System Using PMU", Advanced Materials Research, Vols. 860-863, pp. 2117-2121, 2014

Online since:

December 2013




[1] J. F. Hauer, C.J.D., L. L. Scharf, Initial results in Prony analysis of power system response signals / IEEE Transactions on Power Systems, 1990. 5(1): pp.80-89.

DOI: https://doi.org/10.1109/59.49090

[2] J. R. Smith, F.F., C. S. Woods, J. F. Hauer, D. J. Trudnowski, Transfer function identification in power system applications / IEEE Transactions on Power Systems, 1993. 8: p.1282–1290.

DOI: https://doi.org/10.1109/59.260866

[3] D. J. Trudnowski, J.M.J., J. F. Hauer, Making Prony analysis more accurate using multiple signals / IEEE Transactions on Power Systems, 1999. 14(1): pp.226-231.

DOI: https://doi.org/10.1109/59.744537

[4] J. Xiao, X.X., J. Han, and J. Wu. Dynamic Tracking of Low-frequency Oscillations with Improved Prony Method in Wide-Area Measurement System / IEEE Power Meeting. 2004. Denver, CO.

DOI: https://doi.org/10.1109/pes.2004.1373012

[5] G. Liu, J.Q., and V. Venkatasubramanian, Oscillation Monitoring System Based on Wide Area Synchrophasors in Power Systems / Proc. Bulk Power System Dynamics and Control, (2007).

DOI: https://doi.org/10.1109/irep.2007.4410548

[6] K. Tomsovic, D.B., V. Venkatasubramanian, A. Bose, Designing the Next Generation of Real-Time Control, Communication and Сomputations for Large Power Systems / IEEE Proceedings, 2005. 93: pp.965-979.

DOI: https://doi.org/10.1109/jproc.2005.847249

[7] Michael G. Anderson, N.Z., John W. Pierre, Richard W. Wies, Bootstrap-Based Confidence Interval Estimates for Electromechanical Modes From Multiple Output Analysis of Measured Ambient Data / IEEE Trans. Power Systems, 2005. 20(2).

DOI: https://doi.org/10.1109/pes.2005.1489237

[8] Messina, A.R., Inter-area Oscillations in Power Systems: A Nonlinear and Nonstationary Perspective / Power Electronics and Power Systems2009: Springer.

[9] Hassan Ghasemi, C.C., Confidence Intervals Estimation in the Identification of Electromechanical Modes from Ambient Noise / IEEE Trans. Power Systems, (2008).

DOI: https://doi.org/10.1109/tpwrs.2008.920195

[10] Ning Zhou, J.W.P., Daniel J. Trudnowski, Ross T. Guttromson, Robust RLS Methods for Online Estimation of Power System Electromechanical Modes / IEEE Trans. Power Systems, 2007. 22(3): pp.1240-1249.

DOI: https://doi.org/10.1109/tpwrs.2007.901104

[11] Ning Zhou, D.J.T., John W. Pierre, William A. Mittelstadt, Electromechanical Mode Online Estimation Using Regularized Robust RLS Methods / IEEE Trans. Power Systems, 2008. 23(4): pp.1670-1680.

DOI: https://doi.org/10.1109/tpwrs.2008.2002173

[12] J. Sanchez-Gasca, J.C., Computation of power system low-order models from time domain simulations using a Hankel matrix / IEEE Trans. Power Systems, 1997. 12(4).

DOI: https://doi.org/10.1109/59.627842

[13] L. Guoping, J.Q., V. Venkatasubramaniar. Oscillation monitoring system based on wide area synchrophasors in power systems / Bulk Power System Dynamics and Control – VII. 2007. iREP Symposium.

DOI: https://doi.org/10.1109/irep.2007.4410548

[14] Chusovitin, P. Transient Prediction and Small-Signal Stability Analysis using PMU-based Power System Identification / IASTED Asian Conference Power and Energy Systems. 2012. Phuket, Thailand.

DOI: https://doi.org/10.2316/p.2012.768-052

[15] Norden E. Huang, Z.S., Steven R. Long, Manli C. Wu, Hsing H. Shih, Quanan Zheng, Nai-Chyuan Yen, Chi Chao Tung, Henry H. Liu, The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis / Proc. R. Soc. Lond., 1998. A(454): pp.903-995.

DOI: https://doi.org/10.1098/rspa.1998.0193

[16] Deering R, K.J.F. The use of masking signal to improve empirical mode decomposition. in IEEE Int. Conf. Acoustics / Speech Signal Processing. (2005).

DOI: https://doi.org/10.1109/icassp.2005.1416051

[17] Senroy N, S.S. Two techniques to enhance empirical mode decomposition for power quality applications / IEEE PES General Meeting. (2007).

DOI: https://doi.org/10.1109/pes.2007.386016

[18] Kundur P. Power System Stability and Control. Power System Engineering Series / Electric Power Research Institute, USA, (1994).