论文标题
智能电表数据的分布网格拓扑和参数的最大似然估计
Maximum likelihood estimation of distribution grid topology and parameters from smart meter data
论文作者
论文摘要
本文定义了用于分配网格的入学矩阵估计的最大似然估计器（MLE），利用仅从常见的，无精神上的测量设备（智能仪表）收集的电压幅度和功率测量值。首先，我们基于电压和当前的相分子测量值提出了网格的模型以及现有的MLE。然后，使用常见假设调整了此问题公式以进行无相位测量。在各种情况下，将这些假设的效果与初始问题进行了比较。最后，在流行的IEEE基准网络上进行的数值实验表明结果有希望的结果。缺少数据会大大破坏估计方法。在现实条件下，不测量电压阶段仅在接收矩阵估计中增加30 \％的误差。此外，在有没有相位的情况下，对测量噪声的敏感性相似。
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.
