Stochastic Diffusion Process-based Multi-Level Monte Carlo for Predictive Reliability Assessment of Distribution System

Abstract

Reliability assessment of electrical distribution systems is an important criterion to determine system performance in terms of interruptions. Probabilistic assessment methods are usually used in reliability analysis to deal with uncertainties. These techniques require a longer execution time in order to account for uncertainty. Multi-Level Monte Carlo (MLMC) is an advanced Monte Carlo Simulation (MCS) approach to improve accuracy and reduce the execution time. This paper provides a systematic approach to model the static and dynamic uncertainties of Time to Failure (TTF) and Time to Repair (TTR) of power distribution components using a Stochastic Diffusion Process. Further, the Stochastic Diffusion Process is integrated into MLMC to estimate the impacts of uncertainties on reliability indices. The Euler Maruyama path discretization applied to evaluate the solution of the Stochastic Diffusion Process. The proposed Stochastic Diffusion Process-based MLMC method is integrated into a systematic failure identification technique to evaluate the distribution system reliability. The proposed method is validated with analytical and Sequential MCS methods for IEEE Roy Billinton Test Systems. Finally, the numerical results show the accuracy and fast convergence rates to handle uncertainties compared to Sequential MCS method.