Learning the Evolution of Correlated Stochastic Power System Dynamics

Tyler E. Maltba, Vishwas Rao, Daniel Adrian Maldonado
7/27/2022
cs.LGmath.DSmath.PR

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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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Cite this paper

@article{maltba2022learning,
  title  = {Learning the Evolution of Correlated Stochastic Power System Dynamics},
  author = {Tyler E. Maltba and Vishwas Rao and Daniel Adrian Maldonado},
  year   = {2022},
  eprint = {2207.13310},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2207.13310v1}
}

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