2024/01/04 by Hongli Zhao, Zhao, Hongli, Tyler E. Maltba +7
Decision Sciences · Engineering · #Applications (stat.AP) #Computational Engineering #Dynamical Systems (math.DS) #Energy Load and Power Forecasting #FOS: Computer and information sciences #FOS: Electrical engineering #FOS: Mathematics #Finance #Power System Reliability and Maintenance #Probabilistic and Robust Engineering Design #Systems and Control (eess.SY) #and Science (cs.CE) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2401.02555
openalex publication_date 2024/01/04 · openalex created_date 2024/01/13 · openalex updated_date 2026/08/01
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.