Estimating Distribution Grid Topologies: A Graphical Learning based Approach

TL;DR

This paper introduces a novel graphical learning method to estimate the topology of distribution grids using voltage measurements, enhancing observability and control without extensive real-time line monitoring.

Contribution

It presents a new graphical learning algorithm based on conditional independence tests that can accurately estimate grid topology under various power flow laws and distributions.

Findings

Effective topology estimation demonstrated on test cases

Algorithm applicable to both DC and AC power flow models

Independent of specific load distribution assumptions

Abstract

Distribution grids represent the final tier in electric networks consisting of medium and low voltage lines that connect the distribution substations to the end-users. Traditionally, distribution networks have been operated in a radial topology that may be changed from time to time. Due to absence of a significant number of real-time line monitoring devices in the distribution grid, estimation of the topology is a problem critical for its observability and control. This paper develops a novel graphical learning based approach to estimate the radial operational grid structure using voltage measurements collected from the grid loads. The learning algorithm is based on conditional independence tests for continuous variables over chordal graphs and has wide applicability. It is proven that the scheme can be used for several power flow laws (DC or AC approximations) and more importantly is independent of the specific probability distribution controlling individual bus power usage. The complexity of the algorithm is discussed and its performance is demonstrated by simulations on distribution test cases.