A Dynamic Harmonic Regression Approach to Power System Modal Identification and Prediction

REFERENCES

• Sanchez Gasca, J.J., Vittal, V., Gibbard, M.J., Messina, A.R., Vowles, D.J., Liu, S., and Annakage, U.D., “Inclusion of higher order terms for small-signal (modal) analysis: Committee report—Task force on assessing the need to include higher order terms for small-signal (modal) analysis,” IEEE Trans. Power Syst., Vol. 20, No. 4, pp. 1886–1904, November 2005.
   Web of Science ®Google Scholar
• Harvey, A., and Koopman, S.J., “Unobserved components models in economics and finance,” IEEE Contr. Syst. Mag., pp. 71–81, 2009.
   Web of Science ®Google Scholar
• Young, P.C., Recursive Estimation and Time-Series Analysis: An Introduction for the Student and Practitioner, 2nd ed., Berlin: Springer-Verlag, pp. 29–136, 2011.
   Google Scholar
• Messina, A.R., and Vittal, V., “A structural time series approach to modeling dynamic trends in power system data,” Proceedings of the 2010 IEEE Power and Energy Society General Meeting Conference, pp. 1–8, San Diego, CA, 22–26 July 2010.
   Google Scholar
• Aggarwal, S.K., Saini, L.M., and Kumar, A., “Day-ahead price forecasting in Ontario electricity market using variable-segmented support vector machine-based model,” Electr. Power Compon. Syst., Vol. 37, No. 5, pp. 495–516, April 2009.
   Web of Science ®Google Scholar
• Mbamalu, G., El-Hawary, F., and El-Hawary, M.E., “Decomposition approach to forecasting electric power system commercial load using an artificial neural network,” Electr. Mach. Power Syst., Vol. 25, No. 8, pp. 875–883, September 1997.
   Google Scholar
• Messina, A.R., Vittal, V., Heydt, G.T., and Browne, T.J., “Nonstationary approaches to trend identification and denoising of measured power system oscillations,” IEEE Trans. Power Syst., Vol. 24, pp. 1798–1807, November 2009.
   Web of Science ®Google Scholar
• Young, P.C., Pedregal, D.J., and Tych, W., “Dynamic harmonic regression,” J. Forecast., Vol. 18, pp. 369–394, 1999.
   Web of Science ®Google Scholar
• Artis, M., Clavel, J.G., Hoffmann, M., and Nachane, D., “Harmonic regression models: A comparative review with applications,” Working Paper Series, Institute for Empirical Research in Economics, University of Zurich, Working Paper No. 333, . September 26, 2007, . available at: http://ssrn.com/abstract=1017519
   Google Scholar
• IEEE Task Force, “Identification of electromechanical modes in power systems,” IEEE Special Publication TP462, . June 2012.
   Google Scholar
• Messina, A.R. (Ed.), Inter-Area Oscillations in Power Systems: A Nonlinear and Non-Stationary Perspective, New York: Springer, 2009.
   Google Scholar
• Barocio, E., Zuniga, P., Vazquez, S., and Betancourt, R., “Simplified recursive Newton-type algorithm for instantaneous modal parameter estimation of sub-synchronous oscillations,” Electr. Power Compon. Syst., Vol. 40, No. 8, pp. 864–880, April 2012.
   Web of Science ®Google Scholar
• Mohammadi, A., Khaloozadeh, H., and Amjadifard, R., “Power system critical eigenvalue estimation using flexibility of subspace system identification,” Electr. Power Compon. Syst., Vol. 40, No. 13, pp. 1501–1521, September 2012.
   Web of Science ®Google Scholar
• Bujosa, M., García-Ferrer, A., and Young, P., “An ARMA representation of unobserved components models under generalized random walk specifications: New algorithms and examples,” Mimeo, pp. 125, March 2002.
   Google Scholar
• Bujosa, M., García-Ferrer, A., and Young, P.C., “Linear dynamic harmonic regression,” Comput. Stat. Data Anal., Vol. 52, pp. 999–1024, 2007.
   Web of Science ®Google Scholar
• Cipra, T., Rubio, A., and Trujillo, J., “Time series analysis: Recursive methods and their modifications for time series with outliers and missing observations,” Extracta Mathematicae, Vol. 6, pp. 64–95, 1991.
   Google Scholar
• Alexandrov, T., Bianconcini, S., Dagum, E.B., Maass, P., and McElroy, T.S., “A review of some modern approaches to the problem of trend extraction,” Econo. Rev., Vol. 31, No. 6, pp. 593–624, October 2011.
   Google Scholar
• Harvey, A., Forecasting Structural Time Series Models and the Kalman Filter, first ed., Cambridge: Cambridge University Press, 1989.
   Google Scholar
• Hauer, J.F., Demeure, C.J., and Sacarf, L.L., “Initial results in Prony analysis of power system response signals,” IEEE Trans. Power Syst., Vol. 5, pp. 80–90, February 1990.
   Web of Science ®Google Scholar
• Trudnowski, D.J., “Order reduction of large-scale linear oscillatory system models,” IEEE Trans. Power Syst. Vol. 9, Vo. 1, pp. 451–458, February 1994.
   Web of Science ®Google Scholar
• Anderson, B.D. O., and Moore, J.B., Optimal Filtering, Englewood Cliffs, NJ: Prentice-Hall, Inc., pp. 12–127, 1979.
   Google Scholar
• Norton, J.P., An Introduction to Identification, New York: Academic Press, pp. 143–206, 1986.
   Google Scholar
• Martínez, E., and Messina, A.R., “Modal analysis of measured inter-area oscillations in the Mexican interconnected system: The July 31, 2008 event,” Proceedings of the 2011 IEEE Power and Energy Society General Meeting Conference, pp. 1–8, San Diego, CA, 24–29 July 2011.
   Google Scholar