@inproceedings{c5d490d3274f492f89ca492b5bf40078,
title = "Data-driven topology estimation with limited sensors in radial distribution feeders",
abstract = "Topology estimation is a central part of the wider state estimation problem in electrical networks. We describe a method for data-driven topology estimation in radial distribution feeders with limited sensors. Our algorithm, based on voltage event correlation, estimates a fixed, unknown topology using voltage magnitude measurements collected over several hours and stored in the high performance time-series Berkeley Tree Database. In addition to a topology estimate, our correlation-based algorithm returns a short, human-interpretable snapshot of measurement data that validates the topology estimate. We test our correlation based algorithm on microsynchrophasor data collected on an operational distribution feeder.",
keywords = "Distribution grid, Electric grid, Situational awareness, State estimation, Synchrophasors, Topology",
author = "Mohini Bariya and {Von Meier}, Alexandra and Aminy Ostfeld and Elizabeth Ratnam",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE Annual Green Technologies Conference, GreenTech 2018 ; Conference date: 04-04-2018 Through 06-04-2018",
year = "2018",
month = jun,
day = "5",
doi = "10.1109/GreenTech.2018.00041",
language = "English",
series = "IEEE Green Technologies Conference",
publisher = "IEEE Computer Society",
pages = "183--188",
booktitle = "Proceedings - 2018 IEEE Annual Green Technologies Conference, GreenTech 2018",
address = "United States",
}