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Hierarchical Agglomerative Clustering of Short-Circuit Faults in Transmission Lines

机译:传输线路短路故障的分层凝聚聚类

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Data mining can play a fundamental role in modern power systems. However, a major problem is to extract useful information from the currently available non-labeled digitized time series. This work proposes a new methodology based on hierarchical clustering for labeling faults that occurred in transmission lines. A graphical user interface can benefit from the complementary information provided by the methodology. These faults are responsible for the majority of the disturbances and cascading blackouts. Simulation results using the public dataset UFPA faults are presented to validate the proposed method.
机译:数据挖掘可以在现代电力系统中发挥基本作用。然而,主要问题是从当前可用的非标记数字化时间序列中提取有用的信息。这项工作提出了一种基于分层聚类的新方法,用于标记传输线中发生的故障。图形用户界面可以受益于方法提供的互补信息。这些故障负责大多数干扰和级联的停电。展示了使用公共数据集UFPA故障的仿真结果以验证所提出的方法。

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