Bayesian Error-in-Variables Models for the Identification of Power Networks

TL;DR

This paper introduces a Bayesian error-in-variables model for accurately identifying power network admittance matrices using noisy voltage and current measurements, incorporating prior information for improved results.

Contribution

It develops a novel Bayesian framework that accounts for measurement noise and prior knowledge, enhancing power network topology estimation accuracy.

Findings

Achieves higher accuracy than existing algorithms

Effectively incorporates prior information and sparsity patterns

Demonstrates robustness with noisy measurement data

Abstract

The increasing integration of intermittent renewable generation, especially at the distribution level,necessitates advanced planning and optimisation methodologies contingent on the knowledge of thegrid, specifically the admittance matrix capturing the topology and line parameters of an electricnetwork. However, a reliable estimate of the admittance matrix may either be missing or quicklybecome obsolete for temporally varying grids. In this work, we propose a data-driven identificationmethod utilising voltage and current measurements collected from micro-PMUs. More precisely,we first present a maximum likelihood approach and then move towards a Bayesian framework,leveraging the principles of maximum a posteriori estimation. In contrast with most existing con-tributions, our approach not only factors in measurement noise on both voltage and current data,but is also capable of exploiting available a priori information such as sparsity patterns and knownline parameters. Simulations conducted on benchmark cases demonstrate that, compared to otheralgorithms, our method can achieve significantly greater accuracy.