Confidence intervals estimation in the identification of electromechanical modes from ambient noise

IEEE Transactions on Power Systems

Volumn 23, Issue 2, 2008, Pages 641-648

  Ghasemi, Hassan ^a   Cañizares, Claudio A ^b  

^a Independent Electricity System Operator IESO   (Canada)

^b UNIVERSITY OF WATERLOO   (Canada)

Author keywords

Modal analysis; Power system monitoring; Power system oscillations; Prediction error methods; System identification

Indexed keywords

ELECTRIC LOAD DISTRIBUTION; ERROR ANALYSIS; MODAL ANALYSIS; MONTE CARLO METHODS; UNCERTAINTY ANALYSIS;

POWER SYSTEM MONITORING; POWER SYSTEM OSCILLATIONS; PREDICTION ERROR METHODS;

ELECTRIC POWER SYSTEMS;

EID: 43849088932     PISSN: 08858950     EISSN: None     Source Type: Journal    
DOI: 10.1109/TPWRS.2008.920195     Document Type: Article

References (25)

1. J. F. Hauer, "The use of Prony analysis to determine modal content and equivalent models for measured power system response," in Proc. IEEE/ Power. Eng. Soc. Symp. Application of Eigenanalysis and Frequency Domain Methods for System Dynamic Performance, Jul. 1989, pp. 105-115.
2. J. R. Snuth, F. Fatehi, C. S. Woods, J. F. Hauer, and D. J. Trudnowski, "Transfer function identification in power system applications," IEEE Trans. Power Syst., vol. 8, no. 3, pp. 1282-1290, Aug. 1993.
3. J. F. Hauer, "Application of Prony analysis to the determination of modal content and equivalent models for measured power system response," IEEE Trans. Power Syst., vol. 6, no. 3, pp. 1062-1068, Aug. 1991.
4. B. J. Bulanowski, J. W. Pierre, S. M. Hietpas, T. L. Sharp, and D. A. Pierre, "A comparison of several system identification methods with application to power systems," in Proc. IEEE. 36th Midwest Symp. Circuits and Systems, Aug. 1993, vol. 1, pp. 64-67.
5. J. W. Pierre, D. J. Trudnowski, and M. Donnelly, "Initial results in electromechanical mode identification from ambient data," IEEE Trans. Power Syst., vol. 12, no. 3, pp. 1245-1251, Aug. 1997.
6. R. W. Wies, J. W. Pierre, and D. J. Trudnowski, "Use of ARMA block processing for estimating stationary low-frequency electromechanical modes of power systems," IEEE Trans. Power Syst., vol. 18, no. 1, pp. 167-173, Feb. 2003.
7. H. Ghasemi, C. A. Cañizares, and A. Moshref, "Oscillatory stability limit prediction using stochastic subspace identification," IEEE Trans. Power Syst., vol. 21, no. 2, pp. 736-745, May 2006.
8. N. Zhou and J. Pierre, "Electromechanical mode estimation of power systems from injected probing signal using a subspace method," in Proc. North Amer. Power Symp. (NAPS), Aug. 2004.
9. R. Wies, J. Pierre, and D. Trudnowski, "Use of least-mean squares (LMS) adaptive filtering technique for estimating low-frequency electromechanical modes in power systems," in Proc. IEEE Power Eng. Soc. General Meeting, Jun. 2004, vol. 2, pp. 1863-1870.
10. R. Wies, J. W. Pierre, and A. Balasubramanian, "Using adaptive stepsize least mean squares (LMS) for estimating low-frequency electromechanical modes in power systems," in Proc. Int. Conf. Probabilistic Methods Applied to Power Systems (PMAPS), Jun. 2006.
11. R. Wies, A. Balasubramanian, and J. W. Pierre, ""Adaptive filtering techiniques for estimating electromechanical modes in power systems," in Proc. Power Eng. Soc. General Meeting, Jun. 2007.
12. M. G. Anderson, N. Zhou, J. W. Pierre,, and R. W. Wies, "Boot-strap-based confidence interval estimates for electromechanical modes from multiple output analysis of measured ambient data," IEEE Trans. Power Syst., vol. 20, no. 2, pp. 943-950, May 2005.
13. M. G. Anderson, J. Pierre, and R. Wies, "Confidence interval estimates for the frequency and damping ratio of electromechanical modes using ambient data," in Proc. North Amer. Power Symp. (NAPS ), Oct. 2002.
14. L. Ljung, System Identification Theory for the User. Englewood Cliffs, NJ: Prentice-Hall, 1999.
15. P. Andersen, "Identification of civil engineering structures using vector ARMA models," Ph.D. dissertation, Aalborg Univ., Aalborg, Denmark, 1997.
16. H. Ghasemi and C. A. Cañizares, "Estimation of uncertainty intervals in the identification of electromechamcal modes from ambient noise," in Proc. Jornadas de IEE, Quito, Ecuador, Aug. 2006, 5 pp.
17. T. Söderström and P. Stocia, System Identification. Cambirdge, U.K.: Prentice-Hall International (U.K.), 1989.
18. S. M. Kay, Modern Spectral Estimation: Theory and Application. Englewood Cliffs, NJ: Prentice-Hall, 1988.
19. H. Ghasemi, "On-line monitoring and oscillatory stability margin prediction in power systems based on system identification," Ph.D. dissertation, Univ. Waterloo, Waterloo, ON, Canada, 2006.
20. H. Ghasemi and C. A. Cañizares, "On-line damping torque estimation and oscillatory stability margin prediction," IEEE Trans. Power Syst., vol. 22, no. 2, pp. 667-674, May 2007.
21. D. J. Trudnowski, "Order reduction of large-scale linear oscillatory system models." IEEE Trans. Power Syst., vol. 9, no. 1, pp. 451-458, Feb. 1994.
22. System Identification Toolbox. [Online]. Available: Http:// www.math-works.com/products/sysid/.
23. P. Kundur, Power System Stability and Control. New York: McGraw-Hill, 1994.
24. S. L. Marple, Digital Spectral Analysis. Englewood Cliffs, NJ: Prentice-Hall, 1987.
25. S. K. M. Kodsi and C. A. Cañizares, Modeling and Simulation of IEEE 14-Bus System With FACTS Controllers, Tech. Rep., Univ. Waterloo, Mar. 2003. [Online]. Available: Http://www.power.uwaterloo.ca.