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  4. Maximum likelihood estimation of distribution grid topology and parameters from Smart Meter data
 
conference paper

Maximum likelihood estimation of distribution grid topology and parameters from Smart Meter data

Laurent, Lisa  
•
Brouillon, Jean-Sebastien  
•
Ferrari-Trecate, Giancarlo  
January 1, 2023
2023 Ieee Pes Grid Edge Technologies Conference & Exposition, Grid Edge
IEEE PES Grid Edge Technologies Conference and Exposition (Grid Edge)

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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