k-ShapeStream: Probabilistic Streaming Clustering for Electric Grid Events
k-ShapeStream: Probabilistic Streaming Clustering for Electric Grid Events
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DOI：
10.1109/powertech46648.2021.9494830
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发表时间：
2021-06
期刊：
影响因子：
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通讯作者：
M. Bariya;A. von Meier;John Paparrizos;M. Franklin
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文献类型：
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作者：
M. Bariya;A. von Meier;John Paparrizos;M. Franklin
我们提出了k-ShapeStream，一种用于流式时间序列数据的聚类方法。除了算法的新奇之外，该方法还代表了一种非常实用的电网数据分析方法，不需要模型假设或地面实况信息，可持续地运行在不断增长的数据集上，并为电网运营商提供直观和有见地的结果。我们证明了k-ShapeStream的有效性，使用几个月的真实的同步相量数据从一个操作的配电网在加州。通过两个案例研究（一）Transformer分接头的变化;（二）电压骤降，我们说明了如何k-ShapeStream协助识别和分析经常性的电网事件，在电网决策的关键任务。
We present k-ShapeStream, a clustering method for streaming time-series data. In addition to the algorithmic novelty, the method represents a highly practical approach for electric grid data analytics, requiring no model assumptions or ground truth information, running sustainably on ever growing datasets, and providing intuitive and insightful results to grid operators. We demonstrate the effectiveness of k-ShapeStream using several months of real synchrophasor data from an operational distribution network in California. Through two case studies on (i) transformer tap changes; and (ii) voltage sags, we illustrate how k-ShapeStream assists in identifying and analyzing recurring grid events, a critical task for decision making in electric grids.