access icon free Spatial-temporal decomposition approach for systematically tracking dominant modes, mode shapes and coherent groups in power systems

© The Institution of Engineering and Technology
Received 11/04/2016, Accepted 13/03/2017, Revised 26/01/2017, Published 17/03/2017

This study proposes a novel spatial-temporal decomposition approach for systematically tracking dominant modes, mode shapes and coherent groups in bulk power systems using measurement data. First, components of the dominant oscillation modes, including frequencies and damping ratios, are identified from measurement data through a proposed recursive continuous wavelet transform. Second, cross-wavelet transform is employed to estimate the mode shapes using the wavelet coefficient of the dominant modes. Furthermore, a reconstructed wavelet coefficient, which integrates the wavelet coefficients of all the estimated dominant modes, is used to identify the coherent groups of generators via the cross-correlation coefficient. The proposed approach is evaluated on the simulation data from 16-machine 68-bus test system and China Southern Power Grid (CSG) as well as the field-measurement data collected from phasor measurement units of CSG. It is demonstrated that the proposed approach performs with high accuracy, robustness and efficiency in tracking dominant modes, mode shapes and coherent groups in the bulk power systems.

Inspec keywords: power grids; wavelet transforms; power system measurement

Other keywords: 16-machine 68-bus test system; cross-wavelet transform; recursive continuous wavelet transform; China Southern Power Grid; spatial-temporal decomposition approach

Subjects: Power system measurement and metering; Integral transforms

References

    1. 1)
      • 11. Rai, S., Lalani, D., Nayak, S.K., et al: ‘Estimation of low-frequency modes in power system using robust modified Prony’, IET Gen. Transm. Distrib., 2016, 10, (6), pp. 1401–1409.
    2. 2)
      • 27. Vahidnia, A., Ledwich, G., Palmer, E., et al: ‘Generator coherency and area detection in large power systems’, IET Gener. Transm. Distrib., 2012, 6, (9), pp. 874–883.
    3. 3)
      • 19. Turunen, J.: ‘A wavelet-based method for estimating damping in power systems’. Ph.D. dissertation, Department of Electrical Engineering and Automation, Aalto University, Espoo, Finland, 2011.
    4. 4)
      • 9. Zhou, N., Trudnowski, D.J., Pierre, J.W., et al: ‘Electromechanical mode online estimation using regularized robust RLS methods’, IEEE Trans. Power Syst., 2008, 23, (4), pp. 1670–1680.
    5. 5)
      • 29. Kamwa, I., Pradhan, A.K., Joós, G.: ‘Robust detection and analysis of power system oscillations using the Teager-Kaiser energy operator’, IEEE Trans. Power Syst., 2011, 26, (2), pp. 323–333.
    6. 6)
      • 24. Wu, T., Venkatasubramanian, V., Pothen, A.: ‘Fast parallel stochastic subspace algorithms for large-scale ambient oscillation monitoring’, IEEE Trans. Smart Grid, 2016, doi: 10.1109/TSG.2016.2608965.
    7. 7)
      • 12. Chauduri, N.R., Chauduri, B.: ‘Damping and relative mode-shape estimation in near real-time through phasor approach’, IEEE Trans. Power Syst., 2011, 26, (1), pp. 364–373.
    8. 8)
      • 6. Kosterev, D.N., Taylor, C.W., Mittelstadt, W.A.: ‘Model validation for the August 10, 1996 WSCC system outage’, IEEE Trans. Power Syst., 1999, 14, (3), pp. 967–979.
    9. 9)
      • 13. Barocio, E., Pal, B.C., Thornhill, N.F., et al: ‘A dynamic mode decomposition framework for global power system oscillation analysis’, IEEE Trans. Power Syst., 2015, 30, (6), pp. 2902–2912.
    10. 10)
      • 2. Kundur, P.: ‘Power system stability and control’ (McGraw Hill, New York, 1994).
    11. 11)
      • 3. Crow, M., Gibbard, M., Messina, A., et al: ‘Identification of electromechanical modes in power systems’. IEEE Technical Report, Task Force on Identification of Electromechanical modes, 2012.
    12. 12)
      • 30. Jiang, T., Bai, L.Q., Li, G.Q., et al: ‘Estimating inter-area dominant oscillation mode in bulk power grid using multi-channel continuous wavelet transform’, J. Modern Power Syst. Clean Energy, 2016, 4, (3), pp. 394–405.
    13. 13)
      • 4. Zhou, N., Pierre, J.W., Hauer, J.F.: ‘Initial results in power system identification from injected probing signals using a subspace method’, IEEE Trans. Power Syst., 2006, 21, (3), pp. 1296–1302.
    14. 14)
      • 25. Klepka, A., Uhl, T.: ‘Identification of modal parameters of non-stationary systems with the use of wavelet based adaptive filtering’, Mech. Syst. Signal Process., 2014, 47, (1–2), pp. 21–34.
    15. 15)
      • 14. Messina, A.R., Vittal, V.: ‘Extraction of dynamic patterns from wide-area measurements using empirical orthogonal functions’, IEEE Trans. Power Syst., 2007, 22, (2), pp. 682–692.
    16. 16)
      • 1. IEEE/CIGRE Joint Task Force on Stability Terms and Definitions: ‘Definition and classification of power system stability’, IEEE Trans. Power Syst., 2004, 19, (3), pp. 1387–1401.
    17. 17)
      • 7. Pourbeik, P., Kundur, P., Taylor, C.: ‘The anatomy of a power grid blackout—Root causes and dynamics of recent major blackouts’, IEEE Power Energy Mag., 2006, 4, (5), pp. 22–29.
    18. 18)
      • 28. Ariff, M.A.M., Pal, B.C.: ‘Coherency identification in interconnected power system—an independent component analysis approach’, IEEE Trans. Power Syst., 2013, 28, (3), pp. 1747–1755.
    19. 19)
      • 20. Dosiek, L., Pierre, J.W.: ‘Estimating electromechanical modes and mode shapes using the multichannel ARMAX model’, IEEE Trans. Power Syst., 2013, 28, (2), pp. 1950–1959.
    20. 20)
      • 36. Ljung, L.: ‘System identification: theory for the user’ (Prentice-Hall, Upper Saddle River, NJ, 1999, 2nd edn.).
    21. 21)
      • 35. Kovacevic, B.D., Milosavljevic, M.M., Veinovic, M.D.: ‘Robust recursive AR speech analysis’, Signal Process., 1995, 44, pp. 125–138.
    22. 22)
      • 37. Rogers, G.: ‘Power systems oscillations’ (Kluwer, Norwell, MA, 2000).
    23. 23)
      • 16. Susuki, Y., Mezić, I.: ‘Nonlinear Koopman modes and coherency identification of coupled swing dynamics’, IEEE Trans. Power Syst., 2011, 26, (4), pp. 1894–1904.
    24. 24)
      • 18. Avdaković, S., Bećirović, E., Nuhanović, A., et al: ‘Generator coherency using the wavelet phase difference approach’, IEEE Trans. Power Syst., 2014, 29, pp. 271–278.
    25. 25)
      • 23. Wu, T., Sarmadi, S.A.N., Venkatasubramanian, V., et al: ‘Fast SVD computations for synchrophasor algorithms’, IEEE Trans. Power Syst., 2016, 31, (2), pp. 1651–1652.
    26. 26)
      • 32. Grinsted, A., Moore, J.C., Jevrejeva, S.: ‘Application of the cross wavelet transform and wavelet coherence to geophysical time series’, Nonlin. Processes Geophys., 2004, 11, pp. 561–566.
    27. 27)
      • 31. Zhou, N., Pierre, J.W., Trudnowski, D.J., et al: ‘Robust RLS methods for online estimation of power system electromechanical modes’, IEEE Trans. Power Syst., 2007, 22, (3), pp. 1240–1249.
    28. 28)
      • 5. Jiang, T., Jia, H.J., Zhao, J.L., et al: ‘Mode matching pursuit for estimating dominant modes in bulk power grid’, IET Gen. Transm. Distrib., 2014, 8, (10), pp. 1677–1686.
    29. 29)
      • 21. Ni, J., Shen, C., Liug, F.: ‘Estimating the electromechanical oscillation characteristics of power system based on measured ambient data utilizing stochastic subspace method’. Proc. IEEE Power and Energy Society General Meeting, 2011, July 2011, pp. 1–7.
    30. 30)
      • 38. Jiang, T., Yuan, H., Jia, H., et al: ‘Stochastic subspace identification-based approach for tracking inter-area oscillatory modes in bulk power system utilizing synchrophasor measurements’, IET Gen. Transm. Distrib, 2015, 16, (9), pp. 2409–2418.
    31. 31)
      • 8. Tripathy, P., Srivastava, S.C., Singh, S.N.: ‘A divide-by-difference-filter based algorithm for estimation of generator rotor angle utilizing synchrophasor measurements’, IEEE Tran. Instrum. Meas., 2010, 59, (6), pp. 1562–1570.
    32. 32)
      • 33. Aarts, R.M., Irwan, R., Janssen, A.J.E.M.: ‘Efficient tracking of the cross-correlation coefficient’, IEEE Trans. Speech Audio Process., 2002, 10, (6), pp. 391–402.
    33. 33)
      • 22. Dosiek, L., Zhou, N., Pierre, J.W., et al: ‘Mode estimation algorithms under ambient conditions: a comparative review’, IEEE Trans. Power Syst., 2013, 28, (2), pp. 779–787.
    34. 34)
      • 26. Jiang, T., Jia, H., Yuan, H., et al: ‘Projection pursuit: a general methodology of wide-area coherency detection in bulk power grid’, IEEE Trans. Power Syst., 2016, 31, (4), pp. 2776–2786.
    35. 35)
      • 17. Rueda, J.L., Juárez, C.A., Erlich, I.: ‘Wavelet-based analysis of power system low-frequency electromechanical oscillations’, IEEE Trans. Power Syst., 2011, 26, (3), pp. 1733–1743.
    36. 36)
      • 15. Tripathy, P., Srivastava, S.C., Singh, S.N.: ‘A modified TLS-ESPRIT-based method for low-frequency mode identification in power systems utilizing synchrophasor measurements’, IEEE Trans. Power Syst., 2011, 26, (2), pp. 719–727.
    37. 37)
      • 10. Zhou, N., Pierre, J.W., Trudnowski, D.: ‘A stepwise regression method for estimating dominant electromechanical modes’, IEEE Trans. Power Syst., 2012, 27, (2), pp. 1051–1059.
    38. 38)
      • 34. CSG Dispatching and Control Center: ‘Operation mode of CSG in 2013’ (China Southern Power Grid, 2013).
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-gtd.2016.0551
Loading

Related content

content/journals/10.1049/iet-gtd.2016.0551
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading