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.
Highlights
Power distribution system reliability can usually be evaluated through analytical or s imulation methods (Conti and Rizzo 2019; Brown 2008)
This paper proposes a new model that improves the Multi-Level Monte Carlo (MLMC) method further to develop drift and diffusion coefficient expressions considering the various uncertainties associated with Time to Failure (TTF) and Time to Repair (TTR)
As it is observed from the structure of the methods proposed, the SAIFI values evaluated with the analytical method does not consider the uncertainties whereas the Sequential MCS (SMCS) method only considers randomness and constant failure rates and repair times for sampling TTF and TTR using exponential distribution
Summary
Power distribution system reliability can usually be evaluated through analytical or s imulation methods (Conti and Rizzo 2019; Brown 2008). The MLMC method constructs the TTF and TTR using Stochastic Differential Equation (SDE) (Huda 2018). The impact of different power system components availability on reliability performance is estimated through the MLMC method (Huda and Živanović 2019b). From the above literature on the MLMC method, it is observed that the diffusion and drift coefficients of stochastic differential equations are assumed as constants tuned with reference to the analytical techniques for a specific level of accuracy. This paper proposes a new model that improves the MLMC method further to develop drift and diffusion coefficient expressions considering the various uncertainties associated with TTF and TTR. C) Modeling of the stochastic variables (TTF and TTR) in two periods of the bathtub curve using the proposed method and case studies are performed on IEEE RBTS test systems.
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