Mahdi Arabsadegh,
Volume 22, Issue 3 (9-2026)
Abstract
This article proposes an innovative framework for enhancing resilience in power grid restoration that integrates dynamic breaker failure modeling with structural topology analysis. Unlike conventional approaches focusing solely on breaker health metrics, our method introduces a novel Structural Importance Coefficient (SIC) quantifying each breaker’s criticality through graph-theoretic measures (betweenness/closeness centrality) and cascading failure impact. The hybrid probabilistic-physical failure model combines Weibull-Bayesian degradation analysis with environmental stressors (humidity, temperature) to estimate real-time malfunction probabilities. A hierarchical optimization algorithm then prioritizes repairs by jointly optimizing SIC and health status, achieving: (1) 28% faster critical load recovery, (2) 40% reduction in repair resource waste via strategic SIC-based allocation, and (3) adaptive microgrid formation under uncertainty. Validated on IEEE 39/118-bus systems, the framework demonstrates superior performance compared to Monte Carlo-based methods (e.g., 35% higher load restoration during storms) while requiring no historical data archives. Key innovations include the SIC metric for topology-aware decision-making and a two-stage optimization protocol balancing local breaker conditions with global network resilience. Practical implementation is highlighted through SCADA-compatible modules.
