Bayesian Error-in-Variables Models for the Identification of Distribution Grids
Autor: | Jean-Sebastien Brouillon, Emanuele Fabbiani, Pulkit Nahata, Keith Moffat, Florian Dorfler, Giancarlo Ferrari-Trecate |
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Rok vydání: | 2023 |
Předmět: |
current measurement
line admittance estimation bayesian inference General Computer Science load modeling Bayesian inference Distribution grids Error-in-variables Line admittance estimation Power systems identification admittance errorin-variables bayes methods power systems identification voltage measurement networks standards systems phasor measurement units distribution grids management |
Zdroj: | IEEE Transactions on Smart Grid, 14 (2) |
ISSN: | 1949-3061 1949-3053 |
DOI: | 10.1109/tsg.2022.3211546 |
Popis: | 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 simulations conducted on benchmark electrical systems demonstrate that, compared to other algorithms, our method can achieve significantly greater accuracy than previously developed methods. ISSN:1949-3053 ISSN:1949-3061 |
Databáze: | OpenAIRE |
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