Understanding power system dynamics is essential for online stability assessment and control applications. Global positioning system-synchronised phasor measurement units and frequency disturbance recorders (FDRs) make power system dynamics visible and deliver an accurate picture of the overall operation condition to system operators. However, in the actual field implementations, some measurement data can be inaccessible for various reasons, for example, most notably failure of communication. In this study, a measurement-based approach is proposed to estimate the missing power system dynamics. Specifically, a correlation coefficient index is proposed to describe the correlation relationship between different measurements. Then, the auto-regressive with exogenous input identification model is employed to estimate the missing system dynamic response. The US Eastern Interconnection is utilised in this study as a case study. The robustness of the correlation approach is verified by a wide variety of case studies as well. Finally, the proposed correlation approach is applied to the real FDR data for power system dynamic response estimation. The results indicate that the correlation approach could help select better input locations and thus improve the response estimation accuracy.

References

1. 1)
  - 20. Bhatt, N.: ‘Synchrophasor-based power system dynamic modeling for stability estimation’. Technical Report 1026456, EPRI, Palo Alto, CA, 2012..
2. 2)
  - 6. Sun, K., Zhou, Q., Liu, Y.: ‘A phase locked loop-based approach to real-time modal analysis on synchrophasor measurements’, IEEE Trans. Smart Grid, 2014, 5, (1), pp. 260–269 (doi: 10.1109/TSG.2013.2258475).
3. 3)
  - 22. Poncet, A., Moschytz, G.S.: ‘Optimal order for signal and system modeling’. IEEE Int. Symp. on Circuits and Systems, 1994, pp. 221–224.
4. 4)
  - 10. Zhian, Z., Chunchun, X., Billian, B.J., et al: ‘Power system frequency monitoring network (FNET) implementation’, IEEE Trans. Power Syst., 2005, 20, (4), pp. 1914–1921 (doi: 10.1109/TPWRS.2005.857386).
5. 5)
  - 27. Ye, Y., Matthew Gardner, R., Liu, Y.: ‘Oscillation analysis in western interconnection using distribution-level phasor measurements’. IEEE PES ISGT, Washington, DC, USA, 2012.
6. 6)
  - 17. Ghahremani, E., Kamwa, I.: ‘Dynamic state estimation in power system by applying the extended Kalman filter with unknown inputs to phasor measurements’, IEEE Trans. Power Syst., 2011, 26, pp. 2556–2566 (doi: 10.1109/TPWRS.2011.2145396).
7. 7)
  - 13. Qi, J., Sun, K., Kang, W.: ‘Optimal PMU placement for power system dynamic state estimation by using empirical observability gramian’, IEEE Trans. Power Syst., PP, (99), pp. 1–14.
8. 8)
  - 19. Ljung, L.: System identification toolbox™ user's guid, Matlab R2014b. pp. 292.
9. 9)
  - 25. Sun, K., Hur, K., Zhang, P.: ‘A new unified scheme for controlled power system separation using synchronized phasor measurements’, IEEE Trans. Power Syst., 2011, 26, (3), pp. 1544–1554 (doi: 10.1109/TPWRS.2010.2099672).
10. 10)
  - 21. Sindelar, R., Babuska, R.: ‘Input selection for nonlinear regression models’, IEEE Trans. Fuzzy Syst., 2004, 12, (5), pp. 688–696 (doi: 10.1109/TFUZZ.2004.834810).
11. 11)
  - 26. Dong, J., Xia, T., Zhang, Y., Weeks, T., Thorp, J.S., Liu, Y.: ‘Monitoring power system disturbances at the distribution level’. IEEE PES General Meeting, Pittsburgh, Pennsylvania, USA, 2008.
12. 12)
  - 17. Angel, A.D., Geurts, P., Ernst, D., Glavic, M., Wehenkel, L.: ‘Estimation of rotor angles of synchronous machines using artificial neural networks and local PMU-based quantities’, Neurocomputing, 2007, 70, (16–18), pp. 2668–2678 (doi: 10.1016/j.neucom.2006.12.017).
13. 13)
  - 16. Kamwa, I., Baraboi, B., Wamkeue, R.: ‘Sensorless ANN-based speed estimation of synchronous generator: improved performance through physically motivated pre-filters’. IJCNN, Canada, 2006, pp. 1710–1718.
14. 14)
  - 28. Zhang, Y., Markham, P., Xia, T., et al: ‘Wide-area frequency monitoring network(FNET) architecture and applications’, IEEE Trans. Smart Grid, 2010, 1, (2), pp. 159–167 (doi: 10.1109/TSG.2010.2050345).
15. 15)
  - 1. Phadke, G., Thorp, J.S.: ‘Synchronized phasor measurements and their applications’ (Springer-Verlag, New York, NY, USA, 2008).
16. 16)
  - 12. Miranda, V., Krstulovic, J., Keko, H., Moreira, C., Pereira, J.: ‘Reconstructing missing data in state estimation with autoencoders’, IEEE Trans. Power Syst., 2012, 27, (2), pp. 604–611 (doi: 10.1109/TPWRS.2011.2174810).
17. 17)
  - 14. Aminifar, F., Shahidehpour, M., Fotuhi-Firuzabad, M., Kamalinia, S.: ‘Power system dynamic state estimation with synchronized phasor measurements’, IEEE Trans. Power Syst., 2014, 63, (2), pp. 352–363.
18. 18)
  - 23. Liu, Y., Sun, K., Liu, Y.: ‘A measurement-based power system model for dynamic response estimation and instability warning’, Electr. Power Syst. Res., 2008, 124, pp. 1–9 (doi: 10.1016/j.epsr.2015.02.013).
19. 19)
  - 10. Kim, J., Tong, L.: ‘On topology attack of a smart grid: undetectable attacks and countermeasures’, IEEE J. Sel. Area Commun., 2013, 31, (7), pp. 1294–1305 (doi: 10.1109/JSAC.2013.130712).
20. 20)
  - 9. Huang, Y., Esmalifalak, M., Nguyen, H., et al: ‘Bad data injection in smart grid: attack and defense mechanisms’, IEEE Commun. Mag., 2013, 51, (1), pp. 27–33 (doi: 10.1109/MCOM.2013.6400435).
21. 21)
  - 11. Gao, P., Wang, M., Ghiocel, S.G., Chow, J.H.: ‘Modeless reconstruction of missing synchrophasor measurements’. IEEE PES General Meeting, 2014.
22. 22)
  - 18. Ljung, L.: ‘System identification: theory for the user 2nd Ed’ (PTR Prentice, New Jersey, USA, 1999), pp. 79–88.
23. 23)
  - 14. Kai, S., Lee, S.T., Pei, Z.: ‘An adaptive power system equivalent for real-time estimation of stability margin using phase-plane trajectories’, IEEE Trans. Power Syst., 2011, 26, (2), pp. 915–923 (doi: 10.1109/TPWRS.2010.2055900).
24. 24)
  - I. Kamwa , R. Grondin , Y. Hebert . Wide-area measurement based stabilizing control of large power systems: a decentralized/hierarchical approach. IEEE Trans. Power Syst. , 1 , 136 - 153
25. 25)
  - 5. Dong, J., Ma, X., Djouadi, S.M., Li, H., Liu, Y.: ‘Frequency prediction of the power systems in FNET based on state-space approach and uncertain basis functions’, IEEE Trans. Power Syst., 2014, 29, (6), pp. 2602–2612 (doi: 10.1109/TPWRS.2014.2319057).
26. 26)
  - R. Diao , K. Sun , V. Vittal . Decision Treebased online voltage security assessment using PMU measurements. IEEE Trans. Power Syst. , 2 , 832 - 839
27. 27)
  - D.J. Trudnowski . Estimating electromechanical mode shape from synchrophasor measurements. IEEE Trans. Power Syst. , 3 , 1188 - 1195
28. 28)
  - A.G. Phadke , R.M. de Moraes . The wide world of wide-area measurement. IEEE Power Energy Mag. , 5 , 52 - 65

Measurement-based correlation approach for power system dynamic response estimation

References

Related content