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

Naeem Md Sami, Mia Naeini

TL;DR

Cascading failures threaten modern power systems, especially under uncertainty from stochastic renewables and cyber-physical risks. The paper surveys ML-based techniques organized by cascade phases—normal, precursor, escalation, and post-termination—covering vulnerability analysis, prediction, corrective actions, and recovery. It contributes a systematic taxonomy, synthesizes diverse ML approaches (e.g., graph neural networks, reinforcement learning, Bayesian models), and identifies gaps such as limited escalation-phase studies and underexplored ML-driven hardening. The work underscores the potential of data-rich power grids to enable proactive monitoring, rapid decision support, and efficient recovery, while highlighting the need for scalable, robust ML solutions and richer datasets.

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.

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

TL;DR

Cascading failures threaten modern power systems, especially under uncertainty from stochastic renewables and cyber-physical risks. The paper surveys ML-based techniques organized by cascade phases—normal, precursor, escalation, and post-termination—covering vulnerability analysis, prediction, corrective actions, and recovery. It contributes a systematic taxonomy, synthesizes diverse ML approaches (e.g., graph neural networks, reinforcement learning, Bayesian models), and identifies gaps such as limited escalation-phase studies and underexplored ML-driven hardening. The work underscores the potential of data-rich power grids to enable proactive monitoring, rapid decision support, and efficient recovery, while highlighting the need for scalable, robust ML solutions and richer datasets.

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.
Paper Structure (22 sections, 1 figure, 1 table)

This paper contains 22 sections, 1 figure, 1 table.

Table of Contents

  1. Introduction
  2. Overview and Significance
  3. Motivation and Contribution
  4. ML-based Cascade Analysis in Normal Phase
  5. Vulnerability Analysis
  6. Network Hardening
  7. Cascade Modeling and Simulations
  8. ML-based Cascade Analysis in Precursor Phase
  9. Initial Fault Analysis
  10. Cascade Prediction
  11. Cascade Size Prediction
  12. Prediction of Cascade Evolution and Temporal Aspects
  13. Predicting Cascade Path and Areas at Risk
  14. Cascade Corrective Measures
  15. Dispatching as the Corrective Measure
  16. ...and 7 more sections

Figures (1)

  • Figure 1: Taxonomy of cascading failure analysis supported by ML methods based on different phases before, during and after cascading failures. The section numbers associated with the categories and sub-categories are marked in each element.