用单一模型零样本预测电网级联故障，识别更脆弱线路。
Inductive Power Grid Cascading Failure Analysis with GRU-Gated Graph Attention
- 用GRU门控图注意力网络融合线路间故障关联
- 在多个未见电网上实现零样本迁移，准确率超传统方法
- 适合电力系统安全评估与故障预警场景
在级联故障发生前识别电网中脆弱输电线路极具挑战：现有方法虽能从级联数据中学习线路间的故障相关性，但均基于单一电网训练与评估，难以迁移到未见电网。本文提出一种单一的门控循环单元（GRU）-门控图注意力网络，在有限训练电网的级联故障数据上联合训练，并直接应用于任意未见电网而无需重训。GRU门控机制决定每轮级联中各节点保留或丢弃的信息。实验表明，该模型在跨时间与跨域设置下均实现零样本迁移。利用模型提取的信息，所识别的脆弱线路数量持续优于既有的结构与电气基线方法。
原文摘要 · Abstract (English)
Identifying vulnerable transmission lines in power grids before a cascading failure occurs is challenging: existing methods can learn inter-line failure correlations from cascade data, but they are trained and evaluated on a single grid, and transferring the learned knowledge to an unseen grid remains an open problem. We address this by training a single Gated Recurrent Unit (GRU)-gated Graph Attention Network on combined cascading failure data from limited training grids and applying it directly to any unseen grid without retraining. A GRU gate controls what information each node retains or discards at each cascade iteration. Empirical evaluation shows that the model transfers zero-shot to multiple new grids spanning inter-time and inter-domain settings. Using information extracted from the trained model, we consistently identify more vulnerable lines than established structural and electrical baselines.
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