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

Yining DONG; Yingxiang LIU; S. Joe QIN

doi:10.1016/j.ifacol.2018.09.277

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

Yining DONG, Yingxiang LIU, S. Joe QIN

Research output: Journal Publications › Journal Article (refereed) › peer-review

4 Citations (Scopus)

Abstract

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 language	English
Pages (from-to)	726-731
Number of pages	6
Journal	IFAC-PapersOnLine
Volume	51
Issue number	18
Early online date	8 Oct 2018
DOIs	https://doi.org/10.1016/j.ifacol.2018.09.277
Publication status	Published - 2018
Externally published	Yes

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.

Keywords

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

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10.1016/j.ifacol.2018.09.277Licence: CC BY-NC-ND

Cite this

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title = "DiCCA with Discrete-Fourier Transforms for Power System Events Detection and Localization",

abstract = "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.",

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

author = "Yining DONG and Yingxiang LIU and QIN, {S. Joe}",

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.",

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AU - DONG, Yining

AU - LIU, Yingxiang

AU - QIN, S. Joe

N1 - 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.

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N2 - 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.

AB - 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.

KW - PMU data

KW - discrete Fourier transform

KW - dynamic-inner canonical correlation analysis

KW - event detection

KW - latent variable modeling

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M3 - Journal Article (refereed)

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

Abstract

Bibliographical note

Keywords

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