# Learning Distribution Grid Topologies: A Tutorial arXiv: https://arxiv.org/abs/2206.10837 ยท HTML: https://mlsanity.com/paper/2206.10837/ ## Key facts - Cited by 3 papers in the map (data through 14 Oct 2026). - Builds on 35 of its 112 references that are in the map. - First posted on arXiv 22 Jun 2022. ## Builds on (most cited first, 30 of 35) - [A Survey on State Estimation Techniques and Challenges in Smart Distribution Systems](https://mlsanity.com/paper/1809.00057/) (2018) - [Urban MV and LV Distribution Grid Topology Estimation via Group Lasso](https://mlsanity.com/paper/1611.01845/) (2016) - [Physics Informed Topology Learning in Networks of Linear Dynamical Systems](https://mlsanity.com/paper/1809.10535/) (2018) - [On Identification of Distribution Grids](https://mlsanity.com/paper/1711.01526/) (2017) - [Graph Algorithms for Topology Identification using Power Grid Probing](https://mlsanity.com/paper/1803.04506/) (2018) - [Graphical Models in Meshed Distribution Grids: Topology estimation, change detection and limitations](https://mlsanity.com/paper/1905.06550/) (2019) - [State and Topology Estimation for Unobservable Distribution Systems using Deep Neural Networks](https://mlsanity.com/paper/2104.07208/) (2021) - [Convex Relaxations and Linear Approximation for Optimal Power Flow in Multiphase Radial Networks](https://mlsanity.com/paper/1406.3054/) (2014) - [Estimating Distribution Grid Topologies: A Graphical Learning based Approach](https://mlsanity.com/paper/1602.08509/) (2016) - [PaToPa: A Data-Driven Parameter and Topology Joint Estimation Framework in Distribution Grids](https://mlsanity.com/paper/1705.08870/) (2017) - [Unbalanced Multi-Phase Distribution Grid Topology Estimation and Bus Phase Identification](https://mlsanity.com/paper/1809.07192/) (2018) - [PaToPaEM: A Data-Driven Parameter and Topology Joint Estimation Framework for Time Varying System in Distribution Grids](https://mlsanity.com/paper/1812.06619/) (2018) - [Distributed reactive power feedback control for voltage regulation and loss minimization](https://mlsanity.com/paper/1303.7173/) (2013) - [Voltage Regulation Algorithms for Multiphase Power Distribution Grids](https://mlsanity.com/paper/1508.06594/) (2015) - [Exact Topology and Parameter Estimation in Distribution Grids with Minimal Observability](https://mlsanity.com/paper/1710.10727/) (2017) - [Inverter Probing for Power Distribution Network Topology Processing](https://mlsanity.com/paper/1802.06027/) (2018) - [Topology Estimation using Graphical Models in Multi-Phase Power Distribution Grids](https://mlsanity.com/paper/1803.06531/) (2018) - [Online Energy Price Matrix Factorization for Power Grid Topology Tracking](https://mlsanity.com/paper/1410.6095/) (2014) - [Data-Driven Approach for Distribution Network Topology Detection](https://mlsanity.com/paper/1504.00724/) (2015) - [Inverse Power Flow Problem](https://mlsanity.com/paper/1610.06631/) (2016) - [Voltage Analytics for Power Distribution Network Topology Verification](https://mlsanity.com/paper/1707.06671/) (2017) - [Topology Learning of Radial Dynamical Systems with Latent Nodes](https://mlsanity.com/paper/1803.02793/) (2018) - [Joint Estimation of Topology and Injection Statistics in Distribution Grids with Missing Nodes](https://mlsanity.com/paper/1804.04742/) (2018) - [Joint Grid Topology Reconfiguration and Design of Watt-VAR Curves for DERs](https://mlsanity.com/paper/1910.03020/) (2019) - [Joint Estimation of Operational Topology and Outages for Unbalanced Power Distribution Systems](https://mlsanity.com/paper/1912.04543/) (2019) - [Topology Detection in Microgrids with Micro-Synchrophasors](https://mlsanity.com/paper/1502.06938/) (2015) - [Distribution System Topology Detection Using Consumer Load and Line Flow Measurements](https://mlsanity.com/paper/1503.07224/) (2015) - [Learning Topology of the Power Distribution Grid with and without Missing Data](https://mlsanity.com/paper/1603.01650/) (2016) - [Learning Topology of Distribution Grids using only Terminal Node Measurements](https://mlsanity.com/paper/1608.05031/) (2016) - [Exact Topology Reconstruction of Radial Dynamical Systems with Applications to Distribution System of the Power Grid](https://mlsanity.com/paper/1703.00847/) (2017) ## Built on by (most cited first, 3 of 3) - [Learning Networks from Wide-Sense Stationary Stochastic Processes](https://mlsanity.com/paper/2412.03768/) (2024) - [Learning efficient representations of complex constraints for scalable optimization](https://mlsanity.com/paper/2603.08283/) (2026) - [Mycelium: A Generalizable Cross-Grid Multi-Task Model for Electrical Distribution Systems](https://mlsanity.com/paper/2609.33120/) (2026) ## Abstract (arXiv) 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.