DiCCA with Discrete-Fourier Transforms for Power System Events Detection and Localization

Yining DONG, Yingxiang LIU, S. Joe QIN

Research output: Journal PublicationsJournal Article (refereed)peer-review

4 Citations (Scopus)


Large wide-area power grids monitoring systems generate a large amount of phasor measurement unit (PMU) data. Single variable analysis methods are often applied to the relative phase angle difference (RPAD) between two PMU locations for event detection. However, the possible locations of the events cannot be identified by such methods. In this paper, dynamic-inner canonical correlation analysis (DiCCA) based discrete Fourier transform method is proposed to detect events in the PMU data and identify the possible locations of the events. A case study on a real PMU dataset demonstrates the effectiveness of the proposed method.
Original languageEnglish
Pages (from-to)726-731
Number of pages6
Issue number18
Early online date8 Oct 2018
Publication statusPublished - 2018
Externally publishedYes

Bibliographical note

This work was supported in part by the Natural Science Foundation of China (61490704), the Fundamental Research Program of the Shenzhen Committee on Science and Innovations (20160207, 20170155), the Post-doctoral Fellowship Fund of the Chinese University of Hong Kong, Shenzhen, and the Texas-Wisconsin-California Control Consortium.


  • PMU data
  • discrete Fourier transform
  • dynamic-inner canonical correlation analysis
  • event detection
  • latent variable modeling


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