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Bayesian Error-in-Variables Models for the Identification of Distribution Grids by Jean-Sebastien Brouillon; Emanuele Fabbiani; Pulkit Nahata; Keith Moffat; Florian Dorfler; Giancarlo Ferrari-Trecate is a Engineering article available to read on EtoBox.

What is Bayesian Error-in-Variables Models for the Identification of Distribution Grids about?

The increasing integration of renewable energy requires a good model of the existing power distribution infrastructure, represented by its admittance matrix. However, a reliable estimate may either be missing or quickly become obsolete, as distribution grids are continuously modified. In this work, we propose a method for estimating the admittance matrix from voltage and current measurements. By focusing on μPMU measurements and partially observed networks, we show that voltage collinearity and noisy samples of all electric variables are the main challenges for accurate identification. Moreover, the accuracy of maximum likelihood estimation is often insufficient in real-world scenarios. To overcome this problem, we develop a flexible Bayesian framework that allows one to exploit different forms of prior knowledge about individual line parameters, as well as network-wide characteristics such as the sparsity of the interconnections. Most importantly, we show how to use maximum likelihood estimates for tuning relevant hyperparameters, hence making the identification procedure self-contained. We also discuss numerical aspects of the maximum a posteriori estimate computation. Realistic

Who reads Bayesian Error-in-Variables Models for the Identification of Distribution Grids?

Author

Jean-Sebastien Brouillon; Emanuele Fabbiani; Pulkit Nahata; Keith Moffat; Florian Dorfler; Giancarlo Ferrari-Trecate

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Published

2023

Language

EN

Field

Engineering (Physical Sciences)