用两阶段深度学习快速识别飓风损毁线路并生成修复计划。
An Integrated Two-Stage Deep-Learning Tool for Rapid Post-Hurricane Damage Identification and Repair Scheduling
- 分两阶段：先识别损毁线路，再生成修复调度
- 损毁任务F1达0.920，时间误差仅4.3分钟
- 适合应急响应团队快速制定抢修方案
飓风灾后评估与修复调度通常依赖计算量大的模拟和优化。本文提出一种集成的两阶段深度学习工具，用于快速识别损毁线路并计算修复计划。基于可获取的离线合成数据集（含IEEE 9500节点测试馈线的1,700个飓风情景），数据包含暴露特征、电网元数据、易损性参数、OpenDSS输出、损毁线路标签及自适应大邻域搜索参考调度。第一阶段对比MLP、ResMLP和GraphSAGE，第二阶段比较MLP、DeepSets和Set Transformer。最终选定的ResMLP-Set Transformer流程将第一阶段误差传递至第二阶段，实现损毁任务F1得分为0.920，成对顺序一致性达0.854，起止时间平均绝对误差分别为4.349分钟和4.486分钟。该工具可为新飓风案例提供快速初始修复日志决策支持。
原文摘要 · Abstract (English)
Post-hurricane damage assessment and repair scheduling can require computationally intensive simulation and optimization. This paper presents an integrated two-stage deep-learning tool for rapid damaged-line identification and repair-schedule computation. An available offline synthetic dataset for the IEEE 9500-node test feeder contains 1,700 hurricane scenarios with exposure features, grid metadata, fragility parameters, OpenDSS outputs, damaged-line labels, and Adaptive Large Neighborhood Search reference schedules. Stage 1 benchmarks MLP, ResMLP, and GraphSAGE, while Stage 2 compares MLP, DeepSets, and Set Transformer. The selected ResMLP-Set Transformer pipeline propagates Stage 1 errors into Stage 2 and achieves a damaged-job F1-score of 0.920, pairwise order agreement of 0.854, and start- and end-time mean absolute errors of 4.349 min and 4.486 min, respectively. The tool provides rapid initial repair-log decision support for new hurricane cases.
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