基于智能插头特征传播的配电网监控

Q2 Energy
Simon Grafenhorst, Kevin Förderer, Veit Hagenmeyer
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引用次数: 0

摘要

智能家居电力硬件使得从配电网收集大量低延迟测量数据成为可能。然而,测量结果并不精确,而且并非每个节点都安装了仪器。因此,必须通过伪测量对测量数据进行修正和补充,以获得配电网准确而完整的图像。因此,我们提出并评估了一种新的配电网监测方法。该方法使用智能插头作为测量设备,并使用特征传播算法为每个电网节点生成伪测量数据。特征传播算法利用配电网中母线的同源性,将已知电压值扩散到整个电网。通过模拟 SimBench 基准电网和 IEEE 37 总线系统,对这种获取伪测量值的新方法进行了评估。与现有的 GINN 算法相比,所提出的方法能以更少的计算量生成更精确的电压伪测量值。这样,每当发生测量时,就能以较低的延迟频繁更新配电网监控。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Distribution grid monitoring based on feature propagation using smart plugs

Smart home power hardware makes it possible to collect a large number of measurements from the distribution grid with low latency. However, the measurements are imprecise, and not every node is instrumented. Therefore, the measured data must be corrected and augmented with pseudo-measurements to obtain an accurate and complete picture of the distribution grid. Hence, we present and evaluate a novel method for distribution grid monitoring. This method uses smart plugs as measuring devices and a feature propagation algorithm to generate pseudo-measurements for each grid node. The feature propagation algorithm exploits the homophily of buses in the distribution grid and diffuses known voltage values throughout the grid. This novel approach to deriving pseudo-measurement values is evaluated using a simulation of SimBench benchmark grids and the IEEE 37 bus system. In comparison to the established GINN algorithm, the presented approach generates more accurate voltage pseudo-measurements with less computational effort. This enables frequent updates of the distribution grid monitoring with low latency whenever a measurement occurs.

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来源期刊
Energy Informatics
Energy Informatics Computer Science-Computer Networks and Communications
CiteScore
5.50
自引率
0.00%
发文量
34
审稿时长
5 weeks
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