Maximum likelihood estimation of distribution grid topology and parameters from smart meter 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.
Cite
@article{arxiv.2210.02217,
title = {Maximum likelihood estimation of distribution grid topology and parameters from smart meter data},
author = {Lisa Laurent and Jean-Sébastien Brouillon and Giancarlo Ferrari-Trecate},
journal= {arXiv preprint arXiv:2210.02217},
year = {2022}
}
Comments
5 pages, Grid Edge 2023 conference