Multiple scale identification of power system oscillations using an improved Hilbert-Huang transform

This paper presents a new algorithm for the identification of spectral properties of power system oscillations using an improved Hilbert-Huang transform. Firstly, a relevant vector machine is used as a preprocessor to extend the length of the signal at both ends. Secondly, the empirical mode decomposition method is utilized to decompose the power system oscillation signal into a set of intrinsic mode functions. Then the maximal overlap discrete wavelet transform is applied to each intrinsic mode function and the selection process is employed to select the optimal intrinsic mode function. Finally, oscillation parameters are identified using the Hilbert transform followed by an instantaneous frequency computation. The analysis of the wide area measurement system data from the massive breakup experienced by the western interconnection shows that the proposed method offers advantages in frequency resolution, and produces more physically meaningful Hilbert spectrum than the original Hilbert-Huang transform method, fast Fourier transform and wavelet transform.

[1]  George Eastman House,et al.  Sparse Bayesian Learning and the Relevan e Ve tor Ma hine , 2001 .

[2]  G.T. Heydt,et al.  A Comparative Assessment of Two Techniques for Modal Identification From Power System Measurements , 2008, IEEE Transactions on Power Systems.

[3]  Baoping Tang,et al.  Research on instantaneous feature of non-stationary signal based on hilbert-huang transform and wavelet packet decomposition , 2006, 2006 8th international Conference on Signal Processing.

[4]  Joe H. Chow,et al.  Performance comparison of three identification methods for the analysis of electromechanical oscillations , 1999 .

[5]  Joe H. Chow,et al.  Performance Comparison of Three Identification Methods for the Analysis of Electromechanical Oscilations , 2007 .

[6]  N. Zhou,et al.  An algorithm for removing trends from power-system oscillation data , 2008, 2008 IEEE Power and Energy Society General Meeting - Conversion and Delivery of Electrical Energy in the 21st Century.

[7]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[8]  Michael E. Tipping.  Sparse Bayesian Learning and the Relevance Vector Machine , 2001, J. Mach. Learn. Res..

[9]  Peter Guttorp,et al.  Wavelet analysis of covariance with application to atmospheric time series , 2000 .

[10]  C. W. Taylor,et al.  Model validation for the August 10, 1996 WSCC system outage , 1999 .

[11]  Junsheng Cheng,et al.  Application of support vector regression machines to the processing of end effects of Hilbert Huang transform , 2007 .

[12]  A. R. Messina,et al.  Nonlinear, non-stationary analysis of interarea oscillations via Hilbert spectral analysis , 2006, IEEE Transactions on Power Systems.

[13]  Rob J Hyndman,et al.  Another look at measures of forecast accuracy , 2006 .

[14]  J. F. Hauer,et al.  Application of Prony analysis to the determination of modal content and equivalent models for measured power system response , 1991 .

[15]  N. Senroy,et al.  An Improved Hilbert–Huang Method for Analysis of Time-Varying Waveforms in Power Quality , 2007, IEEE Transactions on Power Systems.

[16]  D.J. Trudnowski,et al.  A Perspective on WAMS Analysis Tools for Tracking of Oscillatory Dynamics , 2007, 2007 IEEE Power Engineering Society General Meeting.

[17]  Lu Chao,et al.  A New Method of Fault Diagnosis for High-Voltage Circuit-Breakers Based on Hilbert-Huang Transform , 2007, 2007 2nd IEEE Conference on Industrial Electronics and Applications.