从智能电表数据的配电网拓扑和参数的最大似然估计

Lisa Laurent, Jean-Sébastien Brouillon, G. Ferrari-Trecate
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引用次数: 0

摘要

本文定义了配电网导纳矩阵估计的最大似然估计器(MLE),利用仅从普通的非同步测量设备(智能电表)收集的电压幅度和功率测量。首先,我们提出了一个网格模型,以及现有的基于电压和电流相量测量的最大似然估计。然后,使用一般假设对该问题公式进行无相测量调整。在各种情况下,将这些假设的效果与初始问题进行比较。最后,在一个常用的IEEE基准网络上进行了数值实验,得到了良好的结果。缺少数据会极大地破坏估计方法。在实际情况下,不测量电压相位只会给导纳矩阵估计增加30%的误差。此外,在有无相位的情况下,对测量噪声的灵敏度相似。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Maximum likelihood estimation of distribution grid topology and parameters from Smart Meter data
This paper defines a Maximum Likelihood Estimator (MLE) for the admittance matrix estimation of distribution grids, utilising voltage magnitude and power measurements collected only from common, unsychronised measuring devices (Smart Meters). First, we present a model of the grid, as well as the existing MLE based on voltage and current phasor measurements. Then, this problem formulation is adjusted for phase-less measurements using common assumptions. The effect of these assumptions is compared to the initial problem in various scenarios. Finally, numerical experiments on a popular IEEE benchmark network indicate promising results. Missing data can greatly disrupt estimation methods. Not measuring the voltage phase only adds 30% of error to the admittance matrix estimate in realistic conditions. Moreover, the sensitivity to measurement noise is similar with and without the phase.
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