面向地震受损配电系统恢复供电的现场团队协调
Field Teams Coordination for Earthquake-Damaged Distribution System Energization
AI总结：
针对地震后配电系统恢复供电问题，提出基于 MDP 的现场团队协调方法，融合行程、供电尝试结果与组件失效概率生成最优策略，并通过剪除非最优动作提升运行效率。
摘要：
灾后场景下配电系统的重新供电至关重要，因为大多数现代基础设施系统都高度依赖电力的存在。本文提出一种协调现场团队的方法，用于在地震引发停电后实现配电系统的最优恢复供电。所提方法利用 Markov 决策过程（MDP）构建最优供电策略，旨在使每个配电系统组件获得供电的预期时间最小化。在状态转移中考虑了每个团队的行程持续时间以及供电尝试的可能结果。系统组件的失效概率通过结构的易损性曲线和峰值地面加速度（PGA）值计算，并经由转移概率编码到 MDP 模型中。此外，所提解决方案提供了若干方法，用于在 MDP 构建过程中识别非最优动作并将其消除，从而在不牺牲解决方案最优性的情况下提高运行时性能。
英文摘要：
The re-energization of electrical distribution systems in a post-disaster scenario is of grave importance as most modern infrastructure systems rely heavily on the presence of electricity. This paper introduces a method to coordinate the field teams for the optimal energization of an electrical distribution system after an earthquake-induced blackout. The proposed method utilizes a Markov Decision Process (MDP) to create an optimal energization strategy, which aims to minimize the expected time to energize each distribution system component. The travel duration of each team and the possible outcomes of the energization attempts are considered in the state transitions. The failure probabilities of the system components are computed using the fragility curves of structures and the Peak Ground Acceleration (PGA) values which are encoded to the MDP model via transition probabilities. Furthermore, the proposed solution offers several methods to determine the non-optimal actions during the construction of the MDP and eliminate them in order to improve the run-time performance without sacrificing the optimality of the solution.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
