Learning Distribution Grid Topologies: A Tutorial

Deka, Deepjyoti; Kekatos, Vassilis; Cavraro, Guido

doi:10.1109/tsg.2023.3271902

Title: Learning Distribution Grid Topologies: A Tutorial

Journal Article · Mon May 01 00:00:00 UTC 2023 · IEEE Transactions on Smart Grid

DOI: https://doi.org/10.1109/tsg.2023.3271902 · OSTI ID:1985626

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Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
Virginia Polytechnic Institute and State University (Virginia Tech), Blacksburg, VA (United States)
National Renewable Energy Laboratory (NREL), Golden, CO (United States)

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. Here 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.

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Research Organization:: National Renewable Energy Laboratory (NREL), Golden, CO (United States)

Sponsoring Organization:: National Science Foundation (NSF); USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE National Nuclear Security Administration (NNSA)

Grant/Contract Number:: 89233218CNA000001; AC36-08GO28308

OSTI ID:: 1985626

Report Number(s):: NREL/JA-5D00-86156; MainId:86929; UUID:c3f96afd-9dde-4968-8270-26dfa79610c4; MainAdminID:69778

Journal Information:: IEEE Transactions on Smart Grid, Journal Name: IEEE Transactions on Smart Grid Journal Issue: 1 Vol. 15; ISSN 1949-3053

Publisher:: IEEECopyright Statement

Country of Publication:: United States

Language:: English

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