Online Learning of Power Transmission Dynamics

TL;DR

This paper introduces a data-driven, real-time method for reconstructing power grid dynamics from PMU data, enhancing system monitoring and stability analysis without prior system knowledge.

Contribution

It develops a convex, maximum likelihood based estimator that adapts to available prior information for dynamic state matrix reconstruction in power grids.

Findings

Method works in near real-time

Requires minimal data

Does not need system parameters

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.