文章基本信息

标题：DiCCA with Discrete-Fourier Transforms for Power System Events Detection and Localization
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作者：Yining Dong ; Yingxiang Liu ; S. Joe Qin 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2018
卷号：51
期号：18
页码：726-731
DOI：10.1016/j.ifacol.2018.09.277
语种：English
出版社：Elsevier
摘要：AbstractLarge 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.
关键词：Keywordsdynamic-inner canonical correlation analysisdiscrete Fourier transformlatent variable modelingPMU dataevent detection