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Bad data detection algorithm for PMU based on spectral clustering

机译:基于频谱聚类的PMU数据检测算法

摘要

A data-driven PMU bad data detection algorithm based on spectral clustering using single PMU data is described. The described algorithm does not require the system topology and parameters. First, a data identification method based on a decision tree is described to distinguish event data and bad data by using the slope feature of each set of data. Then, a bad data detection method based on spectral clustering is described. By analyzing the weighted relationships among all the data, this method can detect the bad data that has a small deviation.
机译:描述了一种基于使用单个PMU数据的频谱聚类的数据驱动的PMU坏数据检测算法。 所描述的算法不需要系统拓扑和参数。 首先,描述基于决策树的数据识别方法来通过使用每组数据的斜率特征来区分事件数据和坏数据。 然后,描述基于频谱聚类的坏数据检测方法。 通过分析所有数据之间的加权关系,该方法可以检测具有小偏差的坏数据。

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