Learning exact topology of a loopy power grid from ambient dynamics

Saurav Talukdar, Deepjyoti Deka, Blake Lundstrom, Misha Chertkov, Murti V. Salapaka

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Scopus citations

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.

Original languageEnglish (US)
Title of host publicatione-Energy 2017 - Proceedings of the 8th International Conference on Future Energy Systems
PublisherAssociation for Computing Machinery, Inc
Pages222-227
Number of pages6
ISBN (Electronic)9781450350365
DOIs
StatePublished - May 16 2017
Event8th ACM International Conference on Future Energy Systems, e-Energy 2017 - Shatin, Hong Kong
Duration: May 16 2017May 19 2017

Publication series

Namee-Energy 2017 - Proceedings of the 8th International Conference on Future Energy Systems

Other

Other8th ACM International Conference on Future Energy Systems, e-Energy 2017
CountryHong Kong
CityShatin
Period5/16/175/19/17

Keywords

  • Dynamics
  • Loopy graphs
  • Power grid
  • Structure learning
  • Swing equations
  • Wiener filltering

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  • Cite this

    Talukdar, S., Deka, D., Lundstrom, B., Chertkov, M., & Salapaka, M. V. (2017). Learning exact topology of a loopy power grid from ambient dynamics. In e-Energy 2017 - Proceedings of the 8th International Conference on Future Energy Systems (pp. 222-227). (e-Energy 2017 - Proceedings of the 8th International Conference on Future Energy Systems). Association for Computing Machinery, Inc. https://doi.org/10.1145/3077839.3077851