Topology Estimation using Graphical Models in Multi-Phase Power Distribution Grids

TL;DR

This paper presents a novel learning framework that reconstructs the operational radial topology of distribution grids using synchronized voltage measurements and conditional independence tests, applicable to unbalanced three-phase power flow.

Contribution

It introduces a new algorithm for topology estimation in distribution grids that handles unbalanced three-phase power flow and exogenous fluctuations.

Findings

Algorithm accurately detects operational lines in simulated tests.

Applicable to a wide range of probability distributions of nodal consumption.

Validated on IEEE distribution grid test cases with AC power flow simulations.

Abstract

Distribution grid is the medium and low voltage part of a large power system. Structurally, the majority of distribution networks operate radially, such that energized lines form a collection of trees, i.e. forest, with a substation being at the root of any tree. The operational topology/forest may change from time to time, however tracking these changes, even though important for the distribution grid operation and control, is hindered by limited real-time monitoring. This paper develops a learning framework to reconstruct radial operational structure of the distribution grid from synchronized voltage measurements in the grid subject to the exogenous fluctuations in nodal power consumption. To detect operational lines our learning algorithm uses conditional independence tests for continuous random variables that is applicable to a wide class of probability distributions of the nodal consumption and Gaussian injections in particular. Moreover, our algorithm applies to the practical case of unbalanced three-phase power flow. Algorithm performance is validated on AC power flow simulations over IEEE distribution grid test cases.