Machine Learning Applications in Cascading Failure Analysis in Power Systems: A Review

TL;DR

This paper reviews how machine learning techniques are increasingly used to analyze, predict, and mitigate cascading failures in power systems, leveraging recent technological advancements and large data sets.

Contribution

It provides a comprehensive categorization and systematic overview of ML-based methods across different phases of cascading failures in power systems.

Findings

ML techniques improve detection and prediction of cascades

Data-driven approaches enhance cascade resiliency strategies

Survey organizes existing research for better understanding

Abstract

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.