Learning Distribution Grid Topologies: A Tutorial

Deepjyoti Deka, Vassilis Kekatos, Guido Cavraro

Research output: Contribution to journalArticlepeer-review

6 Scopus Citations


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.
Original languageAmerican English
Pages (from-to)999-1013
Number of pages15
JournalIEEE Transactions on Smart Grid
Issue number1
StatePublished - 2024

NREL Publication Number

  • NREL/JA-5D00-86156


  • distribution networks
  • learning
  • learning topology
  • topology estimation


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