Learning Distribution Grid Topologies: A Tutorial

TL;DR

This tutorial reviews recent methods for identifying distribution grid topologies from data, emphasizing approaches that address measurement limitations and leverage physical laws, with insights into current techniques and future research directions.

Contribution

It consolidates and contrasts recent topology identification methods, highlighting solutions that overcome measurement constraints and utilize power-flow physics for distribution grids.

Findings

Methods range from least-squares to convex optimization and mixed-integer programming.

Feeder identifiability depends on meter placement and measurement strategies.

Extensions to meshed and multiphase circuits are discussed.

Abstract

Unveiling feeder topologies from data is of paramount importance to advance situational awareness and proper utilization of smart resources in power distribution grids. This tutorial summarizes, contrasts, and establishes useful links between recent works on topology identification and detection schemes that have been proposed for power distribution grids. The primary focus is to highlight methods that overcome the limited availability of measurement devices in distribution grids, while enhancing topology estimates using conservation laws of power-flow physics and structural properties of feeders. Grid data from phasor measurement units or smart meters can be collected either passively in the traditional way, or actively, upon actuating grid resources and measuring the feeder's voltage response. Analytical claims on feeder identifiability and detectability are reviewed under disparate meter placement scenarios. Such topology learning claims can be attained exactly or approximately so via algorithmic solutions with various levels of computational complexity, ranging from least-squares fits to convex optimization problems, and from polynomial-time searches over graphs to mixed-integer programs. Although the emphasis is on radial single-phase feeders, extensions to meshed and/or multiphase circuits are sometimes possible and discussed. This tutorial aspires to provide researchers and engineers with knowledge of the current state-of-the-art in tractable distribution grid learning and insights into future directions of work.