Cascading failures pose a significant threat to power grids and have garnered considerable research interest in the power system domain. The inherent uncertainty and severe impact associated with cascading failures have raised concerns, prompting the development of various techniques to study these complex phenomena. In recent years, advancements in monitoring technologies and the availability of large volumes of data from power systems, coupled with the emergence of intelligent algorithms, have made machine learning (ML) techniques increasingly attractive for addressing cascading failure problems. This survey provides a comprehensive overview of ML-based techniques for analyzing cascading failures in power systems. The survey categorizes these techniques based on the evolutionary phases of the cascade process in power systems, as well as studies focusing on cascade resiliency before the occurrence of cascades and problems related to cascades after their termination. By organizing these works into relevant categories, this survey aims to identify problems related to different phases of cascading failure in power systems that can be addressed by ML.

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机器学习在电力系统级联故障分析中的应用：综述

级联故障对电网构成重大威胁，并引起了电力系统领域的广泛研究兴趣。与级联故障相关的固有不确定性和严重影响引起了人们的关注，促进了研究这些复杂现象的各种技术的发展。近年来，监测技术的进步和电力系统大量数据的可用性，加上智能算法的出现，使得机器学习（ML）技术对于解决级联故障问题越来越有吸引力。本次调查全面概述了用于分析电力系统级联故障的基于机器学习的技术。该调查根据电力系统中级联过程的演化阶段对这些技术进行了分类，并重点研究了级联发生前的级联弹性以及级联终止后与级联相关的问题。通过将这些工作组织到相关类别中，本次调查旨在识别与电力系统中不同阶段的级联故障相关的问题，这些问题可以通过机器学习来解决。