标题： 以恢复力增强为指标量化地震结构健康监测在电力系统震后恢复中的价值 显示英文标题

标题： Quantifying the Value of Seismic Structural Health Monitoring for post-earthquake recovery of electric power system in terms of resilience enhancement

摘要： 地震后的电力网络(EPN)恢复对社区的恢复力至关重要。传统的恢复过程通常依赖于长时间且不精确的人工检查来进行损坏诊断，导致修复优先级不佳和持续的服务中断。地震结构健康监测(SSHM)通过实现更准确和及时的损坏评估，有望加快恢复进程。然而，SSHM的部署会带来成本，其系统级恢复力效益仍研究不足。本研究提出了一种概率模拟框架，以量化SSHM在提高EPN恢复力方面的价值。该框架包括基于网络配置、灾害强度、易损性函数和损坏-功能映射的地震损坏建模，并结合考虑资源限制、修复和转移时间的恢复模拟。系统功能通过基于图的岛屿检测和最优功率流分析进行评估。恢复力通过从功能恢复曲线中得出的恢复力缺乏(LoR)指标进行量化。SSHM通过改变修复调度中使用的损坏信息质量来纳入框架。不同的监测场景(例如，无SSHM基准、部分SSHM、具有不同准确性的全SSHM)使用混淆矩阵进行建模，以模拟损坏误分类。结果表明，通过SSHM提高的损坏意识显著加速了恢复，并将LoR降低了高达21%。这项工作为关键基础设施中SSHM的部署提供了基于证据的决策支持。 显示英文摘要

摘要： Post-earthquake recovery of electric power networks (EPNs) is critical to community resilience. Traditional recovery processes often rely on prolonged and imprecise manual inspections for damage diagnosis, leading to suboptimal repair prioritization and extended service disruptions. Seismic Structural Health Monitoring (SSHM) offers the potential to expedite recovery by enabling more accurate and timely damage assessment. However, SSHM deployment incurs costs, and its system-level resilience benefit remains underexplored. This study proposes a probabilistic simulation framework to quantify the value of SSHM for enhancing EPN resilience. The framework includes seismic damage modeling based on network configuration, hazard intensity, fragility functions, and damage-functionality mappings, combined with recovery simulations incorporating resource constraints, repair and transfer durations. System functionality is evaluated using graph-based island detection and optimal power flow analysis. Resilience is quantified via the Lack of Resilience (LoR) metric derived from the functionality restoration curve. SSHM is incorporated by altering the quality of damage information used in repair scheduling. Different monitoring scenarios (e.g., no-SSHM baseline, partial SSHM, full SSHM with various accuracies) are modeled using confusion matrices to simulate damage misclassification. Results show that improved damage awareness via SSHM significantly accelerates recovery and reduces LoR by up to 21%. This work supports evidence-based decisions for SSHM deployment in critical infrastructure.