Artificial Intelligence in Power System Security and Stability Analysis: A Comprehensive Review

TL;DR

This comprehensive review explores how artificial intelligence techniques improve the security and stability analysis of modern power systems by enabling real-time predictions, risk assessments, and optimized control strategies, especially amid renewable energy integration.

Contribution

It provides an extensive overview of AI applications in power system security, emphasizing decision trees and data-driven methods for real-time, accurate system behavior predictions.

Findings

AI enhances real-time security assessments

Decision trees effectively connect operational data with security metrics

AI-driven methods improve power system reliability and resilience

Abstract

This review comprehensively examines the integration of artificial intelligence (AI) in enhancing the dynamic security assessments of modern power systems. It highlights the pivotal role of AI in facilitating scenario generation, incident prediction, risk assessment, and severity grading, thereby addressing the complexities introduced by renewable energy integration and advancements in digital grid technologies. The paper delves into data-driven techniques, with a particular focus on decision trees that effectively bridge operational characteristics with security metrics. These methodologies enable real-time, accurate predictions of system behaviors under varied operational conditions and support the optimization of control strategies. Through detailed analysis, we demonstrate how AI applications can transform traditional security assessment protocols, enhancing both the efficacy and efficiency of power system operations. The findings advocate for the potential of AI to significantly enhance the reliability and resilience of electrical grids, marking a paradigm shift towards more adaptive and intelligent power infrastructure.