Online learning of power transmission dynamics

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

    Результат исследований: Глава в книге, отчете, сборнике статейМатериалы для конференциирецензирование

    8 Цитирования (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.

    Язык оригиналаАнглийский
    Название основной публикации20th Power Systems Computation Conference, PSCC 2018
    ИздательInstitute of Electrical and Electronics Engineers Inc.
    ISBN (печатное издание)9781910963104
    СостояниеОпубликовано - 20 авг. 2018
    Событие20th Power Systems Computation Conference, PSCC 2018 - Dublin, Ирландия
    Продолжительность: 11 июн. 201815 июн. 2018

    Серия публикаций

    Название20th Power Systems Computation Conference, PSCC 2018


    Конференция20th Power Systems Computation Conference, PSCC 2018


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