Learning the Evolution of Correlated Stochastic Power System Dynamics

07/27/2022

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by   Tyler E. Maltba, et al.

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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.