通过因果推断预测电网故障传播，揭示远距离依赖关系。
Cascading Failure Prediction via Causal Inference
- 构建有向隐含图，用因果边捕捉线路间非局部依赖
- 在3个标准电网系统上验证，可识别最可能和最严重的连锁故障
- 适合电力系统安全分析人员，尤其关注复杂耦合机制的研究者
因果推断提供了一种分析交互主体网络中因果关系的框架。本文提出一种新方法，用于分析电力传输网络中的连锁故障。该方法生成一个有向隐含图，节点代表输电线路，有向边表示因果关系。该图结构不同于系统的物理拓扑，表明输电线路间存在局部与非局部的复杂依赖关系，比仅基于拓扑的模型更全面。本文正式建立了因果推断框架，用于预测新兴异常在系统中的传播路径。基于此框架，设计了两种算法，可提供分析最可能及最昂贵连锁故障场景的理论支持。该框架在IEEE 14-bus、39-bus和118-bus系统上与现有文献对比，验证了其有效性。
原文摘要 · Abstract (English)
Causal inference provides an analytical framework to identify and quantify cause-and-effect relationships among a network of interacting agents. This paper offers a novel framework for analyzing cascading failures in power transmission networks. This framework generates a directed latent graph in which the nodes represent the transmission lines and the directed edges encode the cause-effect relationships. This graph has a structure distinct from the system's topology, signifying the intricate fact that both local and non-local interdependencies exist among transmission lines, which are more general than only the local interdependencies that topological graphs can present. This paper formalizes a causal inference framework for predicting how an emerging anomaly propagates throughout the system. Using this framework, two algorithms are designed, providing an analytical framework to identify the most likely and most costly cascading scenarios. The framework's effectiveness is evaluated compared to the pertinent literature on the IEEE 14-bus, 39-bus, and 118-bus systems.
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