Title: Cascading Failure Prediction via Causal Inference Show Chinese title

Title: 通过因果推理的级联故障预测

Abstract: 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. Show Chinese abstract

Abstract: 因果推断提供了一个分析框架，用于识别和量化相互作用代理网络中的因果关系。 本文提出了一种新的框架，用于分析电力传输网络中的级联故障。 该框架生成一个有向潜在图，其中节点表示输电线路，有向边编码因果关系。 该图的结构不同于系统的拓扑结构，表明了输电线路之间存在局部和非局部相互依赖性的复杂事实，这比拓扑图所能表现的仅局部相互依赖性更为普遍。 本文形式化了一个因果推断框架，用于预测新兴异常在整个系统中的传播方式。 使用该框架，设计了两种算法，提供了一个分析框架来识别最可能和最昂贵的级联场景。 该框架的有效性在IEEE 14母线、39母线和118母线系统上与相关文献进行了比较。