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Online Learning of Power Transmission Dynamics

In this paper, we consider the problem of reconstruction of the dynamic state matrix in a transmission power grid from time-stamped PMU measurements in the regime of ambient fluctuations. Using the maximum likelihood 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.

Author(s):

Andrey Lokhov    
Los Alamos National Laboratory
United States

Marc Vuffray    
Los Alamos National Laboratory
United States

Dmitry Shemetov    
University of California at Davis
United States

Deepjyoti Deka    
Los Alamos National Laboratory
United States

Michael Chertkov    
Los Alamos National Laboratory
United States

 

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