Variants of Hilbert–Huang Transform with Applications to Power Systems’ Oscillatory Dynamics

  • Dina Shona Laila
  • Arturo Roman Messina
  • Bikash Chandra Pal
Part of the Power Electronics and Power Systems book series (PEPS)


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 
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.



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.


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Copyright information

© Springer Science+Business Media,LLC 2009

Authors and Affiliations

  • Dina Shona Laila
    • 1
  • Arturo Roman Messina
    • 1
  • Bikash Chandra Pal
    • 1
  1. 1.Department of Electrical and Electronic EngineeringImperial College LondonLondonUK

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