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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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Type
conference paper
DOI
10.1109/GridEdge54130.2023.10102720
Web of Science ID

WOS:000995195900017

Author(s)
Laurent, Lisa  
Brouillon, Jean-Sebastien  
Ferrari-Trecate, Giancarlo  
Date Issued

2023-01-01

Publisher

IEEE

Publisher place

New York

Published in
2023 Ieee Pes Grid Edge Technologies Conference & Exposition, Grid Edge
ISBN of the book

978-1-6654-6012-5

Subjects

Computer Science, Interdisciplinary Applications

•

Engineering, Electrical & Electronic

•

Computer Science

•

Engineering

Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
SCI-STI-GFT  
Event nameEvent placeEvent date
IEEE PES Grid Edge Technologies Conference and Exposition (Grid Edge)

San Diego, CA

Apr 10-13, 2023

Available on Infoscience
June 19, 2023
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/198317
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