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

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

3 Scopus Citations
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Original languageEnglish
Pages (from-to)726-731
Journal / PublicationIFAC-PapersOnLine
Issue number18
Online published8 Oct 2018
Publication statusPublished - 2018
Externally publishedYes


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.

Research Area(s)

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