论文标题
GraphPMU：通过图表表示的事件聚类，使用位置分布分布级的基本和谐波PMU测量
GraphPMU: Event Clustering via Graph Representation Learning Using Locationally-Scarce Distribution-Level Fundamental and Harmonic PMU Measurements
论文作者
论文摘要
本文涉及确定由分布级相量测量单元（D-PMU）捕获的事件类型和原因的复杂任务，以增强功率分配系统中的情境意识。我们的目标是应对该领域的两个基本挑战：a）由于从D-PMUS购买，安装和流媒体数据的高昂成本，测量地点的稀缺性； b）由于事件是多样化，不经常且本质上不定期的事实，因此对事件签名的先验知识有限。为了应对这些挑战，我们提出了一种称为GraphPMU的无监督图代表性学习方法，以通过提出以下两个新方向：1）使用有关少数可用的Phasor测量值的相对位置的拓扑信息来显着提高事件聚类的性能，从而在位置降低的数据可用性下可用。 2）不仅利用常用的基本相量测量值，在分析各种事件的签名的过程中，总线还使用了较少探索的谐波相量测量值。通过对几个案例研究的详细分析，我们表明GraphPMU可以高于文献中普遍的方法。
This paper is concerned with the complex task of identifying the type and cause of the events that are captured by distribution-level phasor measurement units (D-PMUs) in order to enhance situational awareness in power distribution systems. Our goal is to address two fundamental challenges in this field: a) scarcity in measurement locations due to the high cost of purchasing, installing, and streaming data from D-PMUs; b) limited prior knowledge about the event signatures due to the fact that the events are diverse, infrequent, and inherently unscheduled. To tackle these challenges, we propose an unsupervised graph-representation learning method, called GraphPMU, to significantly improve the performance in event clustering under locationally-scarce data availability by proposing the following two new directions: 1) using the topological information about the relative location of the few available phasor measurement units on the graph of the power distribution network; 2) utilizing not only the commonly used fundamental phasor measurements, bus also the less explored harmonic phasor measurements in the process of analyzing the signatures of various events. Through a detailed analysis of several case studies, we show that GraphPMU can highly outperform the prevalent methods in the literature.
