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arXiv · 2207.13310

Learning the Evolution of Correlated Stochastic Power System Dynamics

Tyler E. Maltba·Vishwas Rao·Daniel Adrian Maldonado
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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.

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Tyler E. Maltba, Vishwas Rao, Daniel Adrian Maldonado. 2022-07-27. Learning the Evolution of Correlated Stochastic Power System Dynamics. https://doi.org/10.1109/pesgm48719.2022.9916982

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