Dimensionality Reduction of Synchrophasor Data for Early Event Detection: Linearized Analysis
Dimensionality Reduction of Synchrophasor Data for Early Event Detection: Linearized Analysis
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DOI：
10.1109/tpwrs.2014.2316476
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发表时间：
2014-11-01
影响因子：
6.6
通讯作者：
Kumar, P. R.
中科院分区：
文献类型：
--
作者：
Xie, Le;Chen, Yang;Kumar, P. R.
本文研究了同步相量数据的基本维数，并提出了一种利用降维后的数据进行早期事件检测的在线应用。首先，分析了相量测量单元（PMU）在正常和异常情况下的数据维数。这表明尽管有大量的原始测量，但潜在的维度非常低。提出了一种基于事件发生时PMU数据核心子空间变化的早期事件检测算法。该算法的理论依据是使用线性动力系统理论。仿真结果验证了该算法的有效性。
This paper studies the fundamental dimensionality of synchrophasor data, and proposes an online application for early event detection using the reduced dimensionality. First, the dimensionality of the phasor measurement unit (PMU) data under both normal and abnormal conditions is analyzed. This suggests an extremely low underlying dimensionality despite the large number of the raw measurements. An early event detection algorithm based on the change of core subspaces of the PMU data at the occurrence of an event is proposed. Theoretical justification for the algorithm is provided using linear dynamical systemtheory. Numerical simulations using both synthetic and realistic PMU data are conducted to validate the proposed algorithm.