@inproceedings{5fbc878a40b047faadeb607365cf04d5,
title = "Learning exact topology of a loopy power grid from ambient dynamics",
abstract = "Estimation of the operational topology of the power grid is necessary for optimal market settlement and reliable dynamic operation of the grid. This paper presents a novel framework for topology estimation for general power grids (loopy or radial) using time-series measurements of nodal voltage phase angles that arise from the swing dynamics. Our learning framework utilizes multivariate Wiener filtering to unravel the interaction between fluctuations in voltage angles at different nodes and identifies operational edges by considering the phase response of the elements of the multivariate Wiener filter. The performance of our learning framework is demonstrated through simulations on standard IEEE test cases.",
keywords = "Dynamics, Loopy graphs, Power grid, Structure learning, Swing equations, Wiener filltering",
author = "Saurav Talukdar and Deepjyoti Deka and Blake Lundstrom and Misha Chertkov and Salapaka, {Murti V.}",
note = "Publisher Copyright: {\textcopyright} 2017 ACM.; 8th ACM International Conference on Future Energy Systems, e-Energy 2017 ; Conference date: 16-05-2017 Through 19-05-2017",
year = "2017",
month = may,
day = "16",
doi = "10.1145/3077839.3077851",
language = "English (US)",
series = "e-Energy 2017 - Proceedings of the 8th International Conference on Future Energy Systems",
publisher = "Association for Computing Machinery, Inc",
pages = "222--227",
booktitle = "e-Energy 2017 - Proceedings of the 8th International Conference on Future Energy Systems",
}