Paper ID: 2207.13310

Learning the Evolution of Correlated Stochastic Power System Dynamics

Tyler E. Maltba, Vishwas Rao, Daniel Adrian Maldonado

A machine learning technique is proposed for quantifying uncertainty in power system dynamics with spatiotemporally correlated stochastic forcing. We learn one-dimensional linear partial differential equations for the probability density functions of real-valued quantities of interest. The method is suitable for high-dimensional systems and helps to alleviate the curse of dimensionality.

Submitted: Jul 27, 2022