Data-Driven Event Identification in the U.S. Power Systems Based on 2D-OLPP and RUSBoosted Trees
Data-Driven Event Identification in the U.S. Power Systems Based on 2D-OLPP and RUSBoosted Trees
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DOI：
10.1109/tpwrs.2021.3092037
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发表时间：
2021-06
影响因子：
6.6
通讯作者：
Shengyuan Liu;Shutang You;Zhenzhi Lin;Chujie Zeng;Hongyu Li;Weikang Wang;Xuetao Hu;Yilu Liu
中科院分区：
文献类型：
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作者：
Shengyuan Liu;Shutang You;Zhenzhi Lin;Chujie Zeng;Hongyu Li;Weikang Wang;Xuetao Hu;Yilu Liu
准确的事件识别是电力系统运行人员态势感知能力的重要组成部分。因此，本文提出了一种适用于电力系统的综合事件识别算法。首先，为了获取和过滤事件识别的合适输入，提出了一种基于频率变化率的事件检测触发器(RoCoF)。然后，利用基于波到达时间差的三角剖分方法，考虑了波传播速度的各向异性，对检测到的事件进行位置估计。其次，提出了一种适用于多种测量数据的基于二维正交保局投影(2D-OLPP)的事件特征提取方法，与传统的一维投影和主成分分析(PCA)方法相比，具有更高的事件特征提取效率。最后，利用基于随机欠采样增强(RUSBoosted)树的分类器来识别检测到的事件类型，该分类器可以缓解数据样本不平衡问题。利用FNET/GridEye的美国电力系统实际测量数据对该方法进行了验证。比较结果表明，本文提出的事件识别算法比现有的事件识别算法具有更好的性能。
Accurate event identification is an essential part of situation awareness ability for power system operators. Therefore, this work proposes an integrated event identification algorithm for power systems. First, to obtain and filter suitable inputs for event identification, an event detection trigger based on the rate of change of frequency (RoCoF) is presented. Then, the wave arrival time difference-based triangulation method considering the anisotropy of wave propagation speed is utilized to estimate the location of the detected event. Next, the two-dimensional orthogonal locality preserving projection (2D-OLPP)-based method, which is suitable for multiple types of measured data, is employed to achieve higher effectiveness in extracting the event features compared with traditional one-dimensional projection and principle component analysis (PCA). Finally, the random undersampling boosted (RUSBoosted) trees-based classifier, which can mitigate the data sample imbalance issue, is utilized to identify the type of the detected event. The proposed approach is demonstrated using the actual measurement data of U.S. power systems from FNET/GridEye. Comparison results show that the proposed event identification algorithm can achieve better performance than existing approaches.