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^*

^*Corresponding author for this work

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

6 Citations (Scopus)

1 Downloads (CityUHK Scholars)

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
Journal	IFAC-PapersOnLine
Volume	51
Issue number	18
Online published	8 Oct 2018
DOIs	https://doi.org/10.1016/j.ifacol.2018.09.277
Publication status	Published - 2018
Externally published	Yes

Research Keywords

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

Publisher's Copyright Statement

This full text is made available under CC-BY-NC-ND 4.0. https://creativecommons.org/licenses/by-nc-nd/4.0/

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10.1016/j.ifacol.2018.09.277

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

author = "Yining Dong and Yingxiang Liu and {Joe Qin}, S.",

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TY - JOUR

T1 - DiCCA with Discrete-Fourier Transforms for Power System Events Detection and Localization

AU - Dong, Yining

AU - Liu, Yingxiang

AU - Joe Qin, S.

PY - 2018

Y1 - 2018

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 - discrete Fourier transform

KW - dynamic-inner canonical correlation analysis

KW - event detection

KW - latent variable modeling

KW - PMU data

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DO - 10.1016/j.ifacol.2018.09.277

M3 - RGC 21 - Publication in refereed journal

SN - 2405-8963

VL - 51

SP - 726

EP - 731

JO - IFAC-PapersOnLine

JF - IFAC-PapersOnLine

IS - 18

ER -

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

Abstract

Research Keywords

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