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• W2767202904 abstract "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." @default.
• W2767202904 created "2017-11-10" @default.
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• W2767202904 date "2017-10-27" @default.
• W2767202904 modified "2023-09-27" @default.
• W2767202904 title "Online Learning of Power Transmission Dynamics" @default.
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