Learning in Power Distribution Grids under Correlated Injections

Sejun Park, Deepjyoti Deka, Michael Chertkov

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    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
    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
    Publication statusPublished - 19 Feb 2019
    Event52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018 - Pacific Grove, United States
    Duration: 28 Oct 201831 Oct 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
    Period28/10/1831/10/18

    Keywords

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

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