2025/06/01 by Jianpeng Chan, Chan, Jianpeng, Iason Papaioannou +3
Computer Science · Engineering · #FOS: Computer and information sciences #Methodology (stat.ME) #Power System Reliability and Maintenance #Smart Grid Security and Resilience #Software Reliability and Analysis Research
paper · pdf · doi:10.48550/arxiv.2506.01044
openalex publication_date 2025/06/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We investigate stratified sampling in the context of network reliability assessment. We propose an unbalanced stratum refinement procedure, which operates on a partition of network components into clusters and the number of failed components within each cluster. The size of each refined stratum and the associated conditional failure probability, collectively termed failure signatures, can be calculated and estimated using the conditional Bernoulli model. The estimator is further improved by determining the minimum number of component failure i^* to reach system failure and then by considering only strata with at least i^* failed components. We propose a heuristic but practicable approximation of the optimal sample size for all strata, assuming a coherent network performance function. The efficiency of the proposed stratified sampler with unbalanced refinement (SSuR) is demonstrated through two network reliability problems.