Learning in Power Distribution Grids under Correlated Injections

Sejun Park, Deepjyoti Deka, Michael Chertkov

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

Abstract

Identifying the operational lines and estimating their impedances are critical problems in distribution grids with applications in fault localization, power flow optimization and others. This paper proposes an exact topology and impedance learning algorithm with low complexity that is able to solve problems using only voltage and injection measurements from the terminal nodes in the grid. The crucial benefit of this approach compared to existing works is that it does not require independence of nodal injections. That is, the proposed algorithm is able to recover the topology and impedances even when injections at the terminal nodes are correlated. In addition, its sample complexity for the accurate recovery is described under the multivariate Gaussian assumption of terminal nodes injections. The performance of our learning algorithm is demonstrated through numerical simulations on both synthetic grids and MATPOWER test grid with linearized and non-linear power flow samples.

Original languageEnglish (US)
Title of host publicationConference Record of the 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018
EditorsMichael B. Matthews
PublisherIEEE Computer Society
Pages1863-1868
Number of pages6
ISBN (Electronic)9781538692189
DOIs
StatePublished - Feb 19 2019
Externally publishedYes
Event52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018 - Pacific Grove, United States
Duration: Oct 28 2018Oct 31 2018

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
Volume2018-October
ISSN (Print)1058-6393

Conference

Conference52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018
Country/TerritoryUnited States
CityPacific Grove
Period10/28/1810/31/18

Keywords

  • Distribution networks
  • Partial observation
  • Sample complexity
  • Topology and impedance estimation

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications

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