English

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

Systems and Control 2022-10-06 v1 Systems and Control Applications

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.

Keywords

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

R2 v1 2026-06-28T02:50:58.426Z