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Learning the Evolution of Correlated Stochastic Power System Dynamics

arXiv 2207.13310 ↗First posted 27 Jul 2022cs.LG math.DS math.PRcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 2 of its 19 references that are in the map. First posted on arXiv 27 Jul 2022.

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References · 2

  1. 2020Nonintrusive Uncertainty Quantification of Dynamic Power Systems Subject to Stochastic Excitationscited by 3
  2. 2020Autonomous learning of nonlocal stochastic neuron dynamicscited by 1

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