  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 and presenting these works into relevant categories, this survey
aims to offer insights and a systematic understanding the role of ML in
mitigating cascading failures in power systems.
