Power system dynamic processes may exhibit highly complex spatial and temporal dynamics and take place over a great range of timescales. When frequency analysis requires the separation of a signal into its essential components, resolution becomes an important issue. The Hilbert–Huang transform (HHT) introduced by Huang is a powerful data-driven, adaptive technique for analyzing data from nonlinear and nonstationary processes. The core to this development is the empirical mode decomposition (EMD) that separates a signal into a series of amplitude- and frequency-modulated signal components from which temporal modal properties can be derived. Previous analytical works have shown that several problems may prevent the effective use of EMD on various types of signals especially those exhibiting closely spaced frequency components and mode mixing. The method allows a precise characterization of temporal modal frequency and damping behavior and enables a better interpretation of nonlinear and nonstationary phenomena in physical terms.
This chapter investigates several extension to the HHT. A critical review of existing approaches to HHT is first presented. Then, a refined masking signal EMD method is introduced that overcomes some of the limitations of the existing approaches to isolate and extract modal components. Techniques to compute a local Hilbert transformation are discussed and a number of numerical issues are discussed.
As case studies, the applications of the various EDM algorithms in power system’ signal analysis are presented. The focus of the case studies is to accurately characterize composite system oscillation in a wide-area power network.
Empirical Mode Decomposition Instantaneous Frequency Composite Signal Empirical Mode Decomposition Method Masking Signal
This is a preview of subscription content, log in to check access
The authors are thankful to Jegatheeswaran Thambirajah and Nina Thornhill from the Chemical Engineering Department, Imperial College London, for useful discussion and good teamwork in pursuing the research in this topic that make it possible for the authors to contribute this chapter. The authors also thank ABB, Switzerland, and National Grid, UK, for the research collaboration done within this research project.
G.Rogers. Power System Oscillations. Kluwer Acad., Boston, 2000.Google Scholar
J. Paserba. Analysis and control of power system oscillation. CIGRE Special Publication 38.01.07, Technical Brochure, Technical Brochure 111, 1996.Google Scholar
P. Kundur. Power System Stability and Control. McGraw Hill, 1994.Google Scholar
P. W. Sauer and M. A. Pai. Power System Dynamics and Stability. Prentice Hall, 1998.Google Scholar
J. F. Hauer, C. J. Demeure, and L. L. Scharf. Initial results in Prony analysis of power system response signals. IEEE Trans. Power Syst., 5(1):80–89, 1990.CrossRefGoogle Scholar
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., 6:1062–1068, 1991.CrossRefGoogle Scholar
D. J. Trudnowski, M. K. Donnelly, and J. F. Hauer. A procedure for oscillatory parameter identification. IEEE Trans. Power Syst., 9(4):2049–2055, 1994.CrossRefGoogle Scholar
I. Kamwa and L. Gerin-Lajoie. State-space system identification toward MIMO models for modal analysis and optimization of bulk power systems. IEEE Trans. Power Syst., 15(1):326–335, 2000.CrossRefGoogle Scholar
D. R. Ostojic and G. T. Heydt. Transient stability assessment by pattern recognition in the frequency domain. IEEE Trans. Power Syst., 6(1):231–237, 1991.CrossRefGoogle Scholar
D. R. Ostojic. Spectral monitoring of power system dynamic performances. IEEE Trans. Power Syst., 8(2):445–451, 1993.CrossRefGoogle Scholar
A. R. Messina and V. Vittal. Nonlinear, non-stationary analysis of interarea oscillations via Hilbert spectral analysis. IEEE Trans. Power Syst., 21(3):1234–1241, 2006.CrossRefGoogle Scholar
A. R. Messina, V. Vittal, D. Ruiz-Vega, and G. Enr’iquez-Harper. Interpretation and visualization of wide-area PMU measurements using Hilbert analysis. IEEE T. Power Syst., 21(4):1763–1771, 2006.CrossRefGoogle Scholar
N. E. Huang, Z. Shen, S. R. Long, M. L. Wu, H. H. Shih, Q. Zheng, N. C. Yen C. C. Tung, and H. H. Liu. The empirical mode decomposition and the Hilbert spectrum for nonlinear and nonstationary time series analysis. Proc. Royal Soc. London, 454:903–995, 1998.CrossRefMATHMathSciNetGoogle Scholar
J. C. Echeverria, J. A. Crowe, M. S. Woolfson, and B. R. Hayes-Gill. Application of empirical mode decomposition to heart rate variability analysis. Med. Biol. Eng. Comput., 39(4):471–479, 2001.CrossRefGoogle Scholar
B. M. Battista, C. Knapp, T. McGee, and V. Goebel. Application of the empirical mode decomposition and Hilbert-Huang transform to seismic reflection data. Geophysics, 72(2):H29–H37, 2007.CrossRefGoogle Scholar
M. A. Andrade, A. R. Messina, C. A. Rivera, and D. Olguin. Identification of instantaneous attributes of torsional shaft signals using the Hilbert transform. IEEE T. Power Syst., 19(3):1422–1429, 2004.CrossRefGoogle Scholar
Z. Wu and N. E. Huang. A study of the characteristics of the white noise using the empirical mode decomposition method. Proc. Royal Soc. London A, 460:1597–1611, 2004.CrossRefMATHGoogle Scholar
R. Deering and J. F. Kaiser. The use of a masking signal to improve empirical mode decomposition. In Proc. IEEE Int. Conf. on Acoustic, Speech and Signal Proc. (ICASSP ’05), 4:485–488, 2005.CrossRefGoogle Scholar
N. Senroy and S. Suryanarayanan. Two techniques to enhance empirical mode decomposition for power quality applications. In Proc. IEEE Power Eng. Soc. Gen. Meet., Tampa, Florida, 1–6, 2007.CrossRefGoogle Scholar
N. Senroy, S. Suryanarayanan, and P. F. Ribeiro. An improved Hilbert-Huang method for analysis of time-varying waveforms in power quality. IEEE Trans. on Power Syst., 22(4):1843–1850, 2007.CrossRefGoogle Scholar
E. Del’echelle, J. Lemoine, and O. Niang. Empirical Mode Decomposition: An analytical approach for sifting process. IEEE Signal Process. Lett., 12:764–767, 2005.CrossRefGoogle Scholar
R. Srinivasan, R. Rengaswamy, and R. Miller. A modified empirical mode decomposition (EMD) process for oscillation characterization in control loops. Control Eng. Pract., 15:1135–1148, 2007.CrossRefGoogle Scholar
R. Deering. Final-Scale Analysis of Speech using Empirical Mode Decomposition: Insight and Applications, PhD Thesis. Duke University, 2006.Google Scholar
W. B. White and S. E. Pazan. Hindcast/forcast of ENSO events based upon the redistribution of observed and model heat content in the western tropical Pacific, 1964–86. Control Phys. Oceanogr., 17:264–280, 1987.CrossRefGoogle Scholar
R. L. C. Spaendonck, F. C. A. Fernandes, R. G. Baraniuk, and J. T. Fokkema. Local Hilbert transformation for seismic attributes. In Proc. EAEG 64th Conference & Technical Exhibition, 2002.Google Scholar
G. Strang and T. Nguyen. Wavelets and Filter Banks. Wellesley - Cambridge Press, 1997.Google Scholar