用时变图网络实时搜索电网连锁故障链，提升抗灾能力。
Real-Time Risky Fault-Chain Search using Time-Varying Graph RNNs
- 将故障搜索建模为部分可观测马尔可夫决策过程，用时变图循环网络求解。
- 能高效捕捉电网时空结构，实现大规模系统的快速风险故障链识别。
- 适合电力系统安全分析、应急响应与智能调度场景的工程师和研究人员。
本文提出一种数据驱动的图形化框架，用于在电力系统中实时搜索高风险的连锁故障链（FCs），以应对气候变化带来的极端天气事件对电网稳定性构成的威胁。随着气候驱动的极端天气频发，及时识别高风险连锁故障链对防止级联失效、保障电网韧性至关重要。然而，电网元件间复杂的时空依赖关系以及随系统规模呈指数增长的搜索空间，给建模与风险故障链搜索带来巨大挑战。为此，本文将搜索过程建模为部分可观测马尔可夫决策过程（POMDP），并采用时变图循环神经网络（GRNN）进行求解。该方法能够有效捕捉系统拓扑与动态所引发的空间-时间结构，并在GRNN隐空间中高效总结系统历史信息，从而实现可扩展且高效的高风险故障链识别。
原文摘要 · Abstract (English)
This paper introduces a data-driven graphical framework for the real-time search of risky cascading fault chains (FCs) in power-grids, crucial for enhancing grid resiliency in the face of climate change. As extreme weather events driven by climate change increase, identifying risky FCs becomes crucial for mitigating cascading failures and ensuring grid stability. However, the complexity of the spatio-temporal dependencies among grid components and the exponential growth of the search space with system size pose significant challenges to modeling and risky FC search. To tackle this, we model the search process as a partially observable Markov decision process (POMDP), which is subsequently solved via a time-varying graph recurrent neural network (GRNN). This approach captures the spatial and temporal structure induced by the system's topology and dynamics, while efficiently summarizing the system's history in the GRNN's latent space, enabling scalable and effective identification of risky FCs.
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