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Enhancements to the Hilbert–Huang Transform for Application to Power System Oscillations

  • Nilanjan Senroy
Chapter
Part of the Power Electronics and Power Systems book series (PEPS)

Abstract

The Hilbert–Huang transform is introduced for time–frequency analysis of oscillatory signals representing power system dynamic behavior. Fundamental assumptions of the Hilbert–Huang transform are revisited, particularly the ability of empirical mode decomposition to yield monocomponent intrinsic mode functions. In the context of the specific application, some enhancements to the original algorithms are discussed. A wide variety of application examples are employed to demonstrate the efficacy of the improved Hilbert–Huang transform.

Keywords

Power System Fast Fourier Transform Empirical Mode Decomposition Instantaneous Frequency High Frequency Component 
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.

Notes

Acknowledgments

The author acknowledges the contribution of Siddharth Suryanarayanan of Colorado School of Mines, Golden, Colorado, USA in the development of the algorithms presented in this chapter. The following other people are also acknowledged for their technical contributions: Paulo M. Ribeiro of Calvin College, Michigan, USA; Michael ‘Mischa’ Steurer of Center for Advanced Power Systems, Florida State University, Tallahassee, Florida, USA; Stephen Woodruff of NASA Dryden Flight Research Center, California, USA; and Arturo Messina of CINVESTAV, Guadalajara, Mexico. Financial support from the Office of Naval Research, USA, the Department of Energy, USA and the Industrial Research and Development Unit, IIT-Delhi, India, is also gratefully acknowledged.

References

  1. 1.
    Huang N E, et al., “The empirical mode decomposition and the Hilbert spectrum for nonlinear and nonstationary time series analysis,” Proc. R. Soc. Lond. A., vol. 454, 1998, pp. 903–995CrossRefMATHGoogle Scholar
  2. 2.
    Gabor D., “Theory of communication,” IEE J. Comm. Eng., vol. 93, 1946, pp. 429–457.Google Scholar
  3. 3.
    Senroy N, Suryanarayanan S, Ribeiro P F, “An improved Hilbert-Huang method for analysis of time-varying waveforms in power quality,” IEEE Trans. Power Sys., vol. 22, No. 4, Nov. 2007, pp. 1843–1850.CrossRefGoogle Scholar
  4. 4.
    Requicha A G, “The zeros of entire functions: theory and engineering applications,” Proc. IEEE, vol. 68, no. 3, Mar. 1980, pp. 308–328.CrossRefGoogle Scholar
  5. 5.
    Deering R, Kaiser J F, “The use of masking signal to improve empirical mode decomposition,” Proc. IEEE Int. Conf. Acoustics, Speech Signal Processing (ICASSP ’05), vol. 454, 2005, pp. 485–488.CrossRefGoogle Scholar
  6. 6.
    Senroy N, Suryanarayanan S, “Two techniques to enhance empirical mode decomposition for power quality applications,” IEEE PES General Meeting, June 2007, pp. 1–6.Google Scholar
  7. 7.
    Messina A R, Vittal V, “Nonlinear, Non-stationary analysis of interarea oscillations via Hilbert spectral analysis,” IEEE Trans. Power Sys., vol. 21, No. 3, Aug. 2006, pp. 1234–1241.CrossRefGoogle Scholar
  8. 8.
    Senroy N, Suryanarayanan S, Steurer M, “Adaptive transfer function estimation of a notional high-temperature superconducting propulsion motor,” Accepted for publication, IEEE Trans. Ind. Appl., Feb. 2008.Google Scholar
  9. 9.
    Senroy N, “Generator coherency using the Hilbert-Huang transform,” IEEE Trans. Power Sys., vol. 23, No. 4, Nov. 2008, pp. 1701–1708.Google Scholar
  10. 10.
    Wang J K, et al., “Analysis of system oscillations using wide-area measurements,” IEEE PES General Meeting, June 2006, pp. 1–6. Google Scholar

Copyright information

© Springer Science+Business Media,LLC 2009

Authors and Affiliations

  1. 1.Department of Electrical EngineeringIndian Institute of TechnologyIndia

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