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Detection and Estimation of Nonstationary Power Transients

  • Gerard Ledwich
  • Ed Palmer
  • Arindam Ghosh
Chapter
Part of the Power Electronics and Power Systems book series (PEPS)

Abstract

This chapter looks at issues of non-stationarity in determining when a transient has occurred and when it is possible to fit a linear model to a non-linear response. The first issue is associated with the detection of loss of damping of power system modes. When some control device such as an SVC fails, the operator needs to know whether the damping of key power system oscillation modes has deteriorated significantly. This question is posed here as an alarm detection problem rather than an identification problem to get a fast detection of a change. The second issue concerns when a significant disturbance has occurred and the operator is seeking to characterize the system oscillation. The disturbance initially is large giving a nonlinear response; this then decays and can then be smaller than the noise level ofnormal customer load changes. The difficulty is one of determining when a linear response can be reliably identified between the non-linear phase and the large noise phase of thesignal. The solution proposed in this chapter uses “Time-Frequency” analysis tools to assistthe extraction of the linear model.

Keywords

False Alarm Power System Probability Density Function False Alarm Rate Instantaneous Frequency 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    R. Wiltshire, P. O’Shea, and G. Ledwich “Rapid Detection of Deteriorating Modal Damping in Power Systems” AUPEC, 2004.Google Scholar
  2. 2.
    E. W. Palmer and G. Ledwich, “Optimal placement of angle transducers in power systems,” IEEE Transactions on Power Systems, Vol. 11, pp. 788–793, 1996.CrossRefGoogle Scholar
  3. 3.
    T. George, J. Crisp, and G. Ledwich, “Advanced tools to manage power system stability in the National Electricity Market,” AUPEC, 2004.Google Scholar
  4. 4.
    D. J. Trudnowski, “Estimating electromechanical mode shape from synchrophasor measurements,” IEEE Transcations on Power Systems, Vol. 23, No. 3, pp. 1188–1195, August 2008.Google Scholar
  5. 5.
    T. Kailath, “An innovations approach to least-squares estimation – Part I: Linear filtering in additive white noise,” IEEE Transactions on Automatic Control, Vol. 13, pp. 646–655, 1968.CrossRefMathSciNetGoogle Scholar
  6. 6.
    R. K. Mehra and J. Peschon, “An innovations approach to fault detection and diagnosis in dynamic systems,” Automatica, Vol. 7, pp. 637–640, 1971.CrossRefGoogle Scholar
  7. 7.
    R. A. Wiltshire, P. O'Shea, and G. Ledwich, “Monitoring of Individual Modal Damping Changes in Multi-Modal Power Systems,” AUPEC, 2004.Google Scholar
  8. 8.
    H. VanTrees, Detection, Estimation and Modulation Theory, Part 1. New York: John Wiley, 1968.Google Scholar
  9. 9.
    P. O’Shea, “A High Resolution Algorithm for Power System Disturbance Monitoring” IEEE Transactions on Power Systems, Vol. 17, No. 3, pp. 676–680, Aug. 2002.CrossRefGoogle Scholar
  10. 10.
    V. Vittal, “Consequence and impact of electric utility industry restructuring on transient stability and small-signal stability analysis,” Proceedings of the IEEE, Vol. 88, pp. 196–207, 2000.CrossRefGoogle Scholar
  11. 11.
    J. F. Hauer, “Application of Prony analysis to the determination of modal content and equivalent models for measured power system response,” IEEE Transactions on Power Systems, Vol. 6, pp. 1062–1068, 1991.CrossRefGoogle Scholar
  12. 12.
    N. Uchida and T. Nagao, “A new eigen-analysis method of steady-state stability studies for large power systems: S matrix method,” IEEE Transactions on Power Systems, Vol. 3, pp. 706–714, 1988.CrossRefGoogle Scholar
  13. 13.
    G. Ledwich and E. Palmer, “Modal estimates from normal operation of power systems,” IEEE Power Engineering Society Winter Meeting, 2000.Google Scholar
  14. 14.
    M. Klein, G. J. Rogers, and P. Kundur, “A fundamental study of inter-area oscillations in power systems,” IEEE Transactions on Power Systems, Vol. 6, pp. 914–921, 1991.CrossRefGoogle Scholar
  15. 15.
    P. Z. Peebles, Probability, random variables, and random signal principles, 4th Ed. New York: McGraw Hill, 2001.Google Scholar
  16. 16.
    R. A. Wiltshire, G. Ledwich, and P. O'Shea “A Kalman Filtering Approach to Rapidly Detecting Modal Changes in Power Systems” IEEE Transactions on Power Systems, Vol. 22, No 4, Paper No: TPWRS-2007.907529.Google Scholar
  17. 17.
    H. Urkowitz, “Energy detection of unknown deterministic signals,” Proceedings of the IEEE, Vol. 55, pp. 523–531, 1967.CrossRefGoogle Scholar
  18. 18.
    R. A. Wiltshire, P. O'Shea, and G. Ledwich, “Monitoring of Individual Modal Damping Changes in Multi-Modal Power Systems,” Journal of Electrical & Electronics Engineering Australia, JEEEA, Vol. 2, pp. 217–222, 2005.Google Scholar
  19. 19.
    J. F. Hauer, C. J. Demuere, and L. J. Scharf, “Initial Results in Prony analysis of power System Response Signals”, IEEE Transactions on Power Systems, Vol. 5, No. 1, pp. 80–89, Feb. 1990.CrossRefGoogle Scholar
  20. 20.
    N. Zhou and J. W. Pierre, “Electromechanical Mode Estimation of Power Systems from Injected Probing Signals using a Subspace Method”, Proceedings of the 35th NAPS Conference, Rolla Mo, Oct. 2003.Google Scholar
  21. 21.
    J. W. Pierre, D. J. Trudnowski, and M. K. Donnelly, “Initial results in Electromechanical Mode Identification from Ambient Data”, IEEE Transactions on Power Systems, Vol. 12, No. 3, pp. 1245–1251, Aug. 1997.CrossRefGoogle Scholar
  22. 22.
    S. M. Kay, Modern Spectral Estimation, Englewood Cliffs, NJ: Prentice-Hall, 1988.MATHGoogle Scholar
  23. 23.
    P. Kundur, Power System Stability and Control, New York: McGraw-Hill, 1994.Google Scholar
  24. 24.
    K. Poon and K. Lee, “Analysis of Transient Stability Swings in Large Interconnected Power Systems by Fourier Transformation”, IEEE Transactions on Power Systems, Vol. 3, No. 4, pp. 1573–1579, Nov. 1988.CrossRefGoogle Scholar
  25. 25.
    P. O’Shea, “The Use of Sliding Spectral Windows for Parameter Estimation in Power System Disturbance Monitoring”, IEEE Transactions on Power Systems, Vol. 15, No. 4, pp. 1261–1267, Nov. 2000.CrossRefGoogle Scholar
  26. 26.
    J. F. Hauer, “Application of Prony Analysis to the Determination of Modal Content and Equivalent Models for Measured Power System Response”, IEEE Transactions on Power Systems, Vol. 6, No. 3, pp. 1062–1068, Aug. 1991.CrossRefGoogle Scholar
  27. 27.
    D. J. Trudnowski and J. E. Dagle, “ Effects of Generator and Static-Load Nonlinearities on Electromechanical Oscillations”, IEEE Transactions on Power Systems, Vol. 12, No. 3, pp. 1283–1289, Aug. 1997.CrossRefGoogle Scholar
  28. 28.
  29. 29.
    B. Boashash, Time Frequency Signal Analysis and Processing – A Comprehensive Reference, Oxford: Elsevier, 2003.Google Scholar
  30. 30.
    B. Boashash and P. J. Black, “An Efficient Real-Time Implementation of the Wigner-Ville Distribution”, IEEE Transactions on Acoustics, Speech and Signal Processing, Vol. 35, pp. 1611–1618, Nov. 1987.CrossRefGoogle Scholar
  31. 31.
    I. Kamwa and L. Gerin-Lajoie, “State-Space System Identification – Toward MIMO Models for Modal Analysis and Optimization of Bulk Power Systems,” IEEE Transactions on Power Systems, Vol. 15, No. 1, pp. 326–335, Feb. 2000.CrossRefGoogle Scholar
  32. 32.
    J. J. Sanchez-Gasca, V. Vittal, M. J. Gibbard, A. R. Messina, D. J. Vowles, S. Liu, and U. D. Annakkage, “Inclusion of Higher Order Terms for Small-Signal (Modal) Analysis – Task Force on Assessing the Need to Include Higher Order Terms for Small-Signal (Modal) Analysis”, IEEE Transactions on Power Systems, Vol. 20, No. 4, pp. 788–793, Nov. 2005.CrossRefGoogle Scholar
  33. 33.
    E. Palmer, Multi-Mode Damping of Power System Oscillations, PhD thesis, The University of Newcastle, 1998.Google Scholar
  34. 34.
    E. W. Palmer and G. Ledwich, “Optimal placement of Angle Transducers in Power Systems”, IEEE Transactions on Power Systems, Vol. 11, No. 2, pp. 788–793, May 1996.CrossRefGoogle Scholar
  35. 35.
    H. Whitehouse, B. Boashash, and J. Speiser, “High resolution processing techniques for temporal and spatial signals”, in High Resolution Techniques in Underwater Acoustics, Lecture Notes in Control and Information Series, New York-Heidelburg-Berlin Springer-Verlag, 1989.Google Scholar
  36. 36.
    B. Lovell, R. Williamson, and B. Boashash, “The Relationship between Instantaneous Frequency and Time-Frequency Representations”, IEEE Transactions on Signal Processing, Vol. 41, No.3, pp. 1458–1461, Mar. 1993.CrossRefMATHGoogle Scholar

Copyright information

© Springer Science+Business Media,LLC 2009

Authors and Affiliations

  1. 1.Faculty of Built Environment and EngineeringQueensland University of TechnologyBrisbaneAustralia

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