Learning the Evolution of Correlated Stochastic Power System Dynamics
Machine Learning
2023-12-19 v1 Dynamical Systems
Probability
Data Analysis, Statistics and Probability
Applications
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.
Cite
@article{arxiv.2207.13310,
title = {Learning the Evolution of Correlated Stochastic Power System Dynamics},
author = {Tyler E. Maltba and Vishwas Rao and Daniel Adrian Maldonado},
journal= {arXiv preprint arXiv:2207.13310},
year = {2023}
}
Comments
5 pages, 2 figures, Accepted to 2022 IEEE PES GM