Online learning of power transmission dynamics

Andrey Y. Lokhov, Marc Vuffray, Dmitry Shemetov, Deepjyoti Deka, Michael Chertkov

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

    8 Citations (Scopus)


    We consider the problem of reconstructing the dynamic state matrix of transmission power grids from time-stamped PMU measurements in the regime of ambient fluctuations. Using a maximum likelihood based approach, we construct a family of convex estimators that adapt to the structure of the problem depending on the available prior information. The proposed method is fully data-driven and does not assume any knowledge of system parameters. It can be implemented in near real-time and requires a small amount of data. Our learning algorithms can be used for model validation and calibration, and can also be applied to related problems of system stability, detection of forced oscillations, generation re-dispatch, as well as to the estimation of the system state.

    Original languageEnglish
    Title of host publication20th Power Systems Computation Conference, PSCC 2018
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    ISBN (Print)9781910963104
    Publication statusPublished - 20 Aug 2018
    Event20th Power Systems Computation Conference, PSCC 2018 - Dublin, Ireland
    Duration: 11 Jun 201815 Jun 2018

    Publication series

    Name20th Power Systems Computation Conference, PSCC 2018


    Conference20th Power Systems Computation Conference, PSCC 2018


    • Parameter learning
    • Phasor measurement units
    • Reconstruction algorithm
    • Swing equations
    • Synchronous measurements
    • Transmission grid dynamics


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