Data-Driven Estimation of Failure Probabilities in Correlated Structure-Preserving Stochastic Power System Models
CoRR(2024)
摘要
We propose a data-driven approach for propagating uncertainty in stochastic
power grid simulations and apply it to the estimation of transmission line
failure probabilities. A reduced-order equation governing the evolution of the
observed line energy probability density function is derived from the
Fokker–Planck equation of the full-order continuous Markov process. Our method
consists of estimates produced by numerically integrating this reduced
equation. Numerical experiments for scalar- and vector-valued energy functions
are conducted using the classical multimachine model under spatiotemporally
correlated noise perturbation. The method demonstrates a more sample-efficient
approach for computing probabilities of tail events when compared with kernel
density estimation. Moreover, it produces vastly more accurate estimates of
joint event occurrence when compared with independent models.
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