Online Learning of Power Transmission Dynamics

Ensuring stable, secure and reliable operations of the power grid is a primary concern for system operators [1]. Security assessment and control actions heavily rely on the accuracy of the assumed power system model and its parameters and of the estimated state [2]. Thus, inaccuracies in state estimation data or in the networked dynamic model can impact the assessment of the system stability and the efficacy of the corresponding control measures. In this paper, we explore the possibility to leverage the proliferation of Phasor Measurement Units (PMUs) that collect time synchronous data in a distributed way, for validating the assumed power system model and the current system state. In particular, our goal is to develop a data-efficient learning framework for performing an online reconstruction of the dynamic model using the minimal number of assumptions and exclusively relying on the PMU measurements. A number of recent works showed promising results in attacking this problem [3], [4], [5], [6], [7], [8], [9]. Here, we propose to extend the scope of existing works to the problem of extracting the dynamic state matrix from PMU measurements in a purely data-driven way, without assuming any knowledge of model parameters. We take advantage of the separation of scales that exists in the regime of ambient fluctuations around the steady state leading to power system dynamics excited by stochastic load variations.