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Growing use of renewable energy, opening up of the energy markets, and incorporation of monitoring systems have opened up opportunities for a more resilient power grid. However, this evolution also brings new challenges such as cyberattacks and voltage instability. Timely analysis of this information is essential, and ML techniques have proven effective in solving power planning problems. This review examines current research using MLT—such as artificial roots, selection of trees, and auxiliary conduit devices—to increase the reliability and safety of energy systems with a focus on dynamic security analytics, disruptive capabilities assessment, and cyberattack detection. The review focuses on successes, strategies, and barriers in classifier construction and implementation of datasets. Additionally, it concludes with an in-depth compelling and compelling study in transient dynamics analysis with insights into the challenges and possible future research directions in this growing field.

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An Inclusive Review of Machine Learning Techniques in Securing Power Systems and Recognition of Cyber Attacks

  • 会议论文

  • 发表日期：

    2026年

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  • Kalyan Acharjya,
  • Devendra Kumar Doda,
  • Vijendra Singh Rawat,
  • D. Ganesh,
  • Surender Reddy Salkuti

摘要

Growing use of renewable energy, opening up of the energy markets, and incorporation of monitoring systems have opened up opportunities for a more resilient power grid. However, this evolution also brings new challenges such as cyberattacks and voltage instability. Timely analysis of this information is essential, and ML techniques have proven effective in solving power planning problems. This review examines current research using MLT—such as artificial roots, selection of trees, and auxiliary conduit devices—to increase the reliability and safety of energy systems with a focus on dynamic security analytics, disruptive capabilities assessment, and cyberattack detection. The review focuses on successes, strategies, and barriers in classifier construction and implementation of datasets. Additionally, it concludes with an in-depth compelling and compelling study in transient dynamics analysis with insights into the challenges and possible future research directions in this growing field.