Maximum likelihood estimation of distribution grid topology and parameters from smart meter data

TL;DR

This paper introduces a maximum likelihood estimator for distribution grid topology and parameters using only voltage magnitude and power data from unsynchronized smart meters, addressing phase measurement challenges and demonstrating promising results on benchmark networks.

Contribution

It develops a novel MLE approach for grid admittance matrix estimation using phase-less measurements, adapting existing models and analyzing the impact of missing phase data.

Findings

Missing voltage phase increases estimation error by 30%.

The method performs well on IEEE benchmark networks.

Measurement noise sensitivity is similar with or without phase data.

Abstract

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.