An Improved Hilbert–Huang Method for Analysis of Time-Varying Waveforms in Power Quality

The Hilbert-Huang method is presented with modifications, for time-frequency analysis of distorted power quality signals. The empirical mode decomposition (EMD) is enhanced with masking signals based on fast Fourier transform (FFT), for separating frequencies that lie within an octave. Further, the instantaneous frequency and amplitude of the constituent modes obtained by Hilbert spectral analysis are improved by demodulation. The method shows promising time-frequency-magnitude localization capabilities for distorted power quality signals. The performance of the new technique is compared with that of another multiresolution analysis tool, the S-transform-a phase corrected wavelet transform. Analysis on actual measurements of transformer inrush current from an existing laboratory setup is used to demonstrate this technique.

[1]  J.S. Smith,et al.  Empirical Mode Decomposition For Power Quality Monitoring , 2005, 2005 IEEE/PES Transmission & Distribution Conference & Exposition: Asia and Pacific.

[2]  Vijay Vittal,et al.  Interpretation and Visualization of Wide-Area PMU Measurements Using Hilbert Analysis , 2006 .

[3]  S. Santoso,et al.  Power quality assessment via wavelet transform analysis , 1996 .

[4]  B. Logan.  Information in the zero crossings of bandpass signals , 1977, The Bell System Technical Journal.

[5]  P.K. Dash,et al.  Multiresolution S-transform-based fuzzy recognition system for power quality events , 2004, IEEE Transactions on Power Delivery.

[6]  A. Requicha,et al.  The zeros of entire functions: Theory and engineering applications , 1980, Proceedings of the IEEE.

[7]  A.R. Messina,et al.  Interpretation and Visualization of Wide-Area PMU Measurements Using Hilbert Analysis , 2006, IEEE Transactions on Power Systems.

[8]  A.R. Messina,et al.  Identification of instantaneous attributes of torsional shaft signals using the Hilbert transform , 2004, IEEE Transactions on Power Systems.

[9]  J. Driesen,et al.  Analysing time-varying power system harmonics using wavelet transform , 1996, Quality Measurement: The Indispensable Bridge between Theory and Reality (No Measurements? No Science! Joint Conference - 1996: IEEE Instrumentation and Measurement Technology Conference and IMEKO Tec.

[10]  G. Panda,et al.  Power Quality Analysis Using S-Transform , 2002, IEEE Power Engineering Review.

[11]  Paulo F. Ribeiro,et al.  Exploring the power of wavelet analysis , 1996 .

[12]  Irene Yu-Hua Gu,et al.  Signal processing of power quality disturbances , 2006 .

[13]  N. Huang,et al.  A new view of nonlinear water waves: the Hilbert spectrum , 1999 .

[14]  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.

[15]  Gerald T. Heydt,et al.  Applications of the windowed FFT to electric power quality assessment , 1999 .

[16]  Alan V. Oppenheim,et al.  Discrete-time Signal Processing. Vol.2 , 2001 .

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

[18]  Gabriel Rilling,et al.  Empirical mode decomposition as a filter bank , 2004, IEEE Signal Processing Letters.

[19]  Jean Claude Nunes,et al.  Image analysis by bidimensional empirical mode decomposition , 2003, Image Vis. Comput..

[20]  Edward J. Powers,et al.  Time-frequency analysis of power system disturbance signals for power quality , 1999, 1999 IEEE Power Engineering Society Summer Meeting. Conference Proceedings (Cat. No.99CH36364).

[21]  L. Cohen,et al.  Time-frequency distributions-a review , 1989, Proc. IEEE.

[22]  James F. Kaiser,et al.  The use of a masking signal to improve empirical mode decomposition , 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..

[23]  Hualou Liang,et al.  Application of the empirical mode decomposition to the analysis of esophageal manometric data in gastroesophageal reflux disease , 2005, IEEE Transactions on Biomedical Engineering.

[24]  Lalu Mansinha,et al.  Localization of the complex spectrum: the S transform , 1996, IEEE Trans. Signal Process..

[25]  M. Steurer.  Real time simulation for advanced time-varying harmonic analysis , 2005, IEEE Power Engineering Society General Meeting, 2005.

[26]  N. Huang,et al.  A study of the characteristics of white noise using the empirical mode decomposition method , 2004, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.