Learning the Evolution of Correlated Stochastic Power System Dynamics
arXiv:2207.13310 · doi:10.1109/PESGM48719.2022.9916982
Abstract
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.
5 pages, 2 figures, Accepted to 2022 IEEE PES GM