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<p>The growing reliance on electrical energy in modern society places increasing demands on power system engineers to ensure secure, stable, and uninterrupted electricity delivery. With the increasing frequency of short- and long-duration power interruptions, there is a critical need for advanced systematic approaches to improve grid resilience and reliability. Among these, self-healing mechanisms have emerged as a key enabler to achieve autonomous event detection, isolation, and restoration in power distribution systems. This literature presents a comprehensive review of the current state of self-healing in smart grids, focusing on its fundamental concepts of smart healing in transmission, distribution, and smart electric grids, including control strategies and technological advancements with artificial intelligence applications. The core contribution of the literature is towards highlighting the current state of self-healing in smart grid technology, emphasizing the AI-driven solutions and their potential for future grid evolution. The paradigm shift from the traditional method to the optimization method to the AI-driven solution has been mapped in this study. This study analyses significant constraints, implementation issues, and research deficiencies in current studies, substantiated by quantitative performance measures and practical assessments. In light of these findings, certain future research trajectories, notably concerning cyber-security, adaptive learning models, sustainability of large networks, hybrid frameworks, and real-time deployment, are delineated. This review provides a clear and analytical viewpoint on AI-enabled self-healing in smart distribution systems, delivering practical insights to inform the construction of robust and intelligent next-generation grids. The reviewed literature includes the most recent publications, which are systematically categorized based on optimization objectives, control actions, communication requirements, and solution methodologies. Furthermore, this literature highlights prominent algorithms and intelligent techniques used for self-healing capabilities and recent advancements in methodologies that offer a comparative analysis to aid researchers and practitioners in selecting appropriate methods.</p>

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Event detection and self-healing in smart grids using artificial intelligence: a comprehensive review

  • 期刊论文

  • 发表日期：

    2026年5月12日

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  • P. Swati Patro,
  • N. Praneeth,
  • S. T. P. Srinivas

摘要

The growing reliance on electrical energy in modern society places increasing demands on power system engineers to ensure secure, stable, and uninterrupted electricity delivery. With the increasing frequency of short- and long-duration power interruptions, there is a critical need for advanced systematic approaches to improve grid resilience and reliability. Among these, self-healing mechanisms have emerged as a key enabler to achieve autonomous event detection, isolation, and restoration in power distribution systems. This literature presents a comprehensive review of the current state of self-healing in smart grids, focusing on its fundamental concepts of smart healing in transmission, distribution, and smart electric grids, including control strategies and technological advancements with artificial intelligence applications. The core contribution of the literature is towards highlighting the current state of self-healing in smart grid technology, emphasizing the AI-driven solutions and their potential for future grid evolution. The paradigm shift from the traditional method to the optimization method to the AI-driven solution has been mapped in this study. This study analyses significant constraints, implementation issues, and research deficiencies in current studies, substantiated by quantitative performance measures and practical assessments. In light of these findings, certain future research trajectories, notably concerning cyber-security, adaptive learning models, sustainability of large networks, hybrid frameworks, and real-time deployment, are delineated. This review provides a clear and analytical viewpoint on AI-enabled self-healing in smart distribution systems, delivering practical insights to inform the construction of robust and intelligent next-generation grids. The reviewed literature includes the most recent publications, which are systematically categorized based on optimization objectives, control actions, communication requirements, and solution methodologies. Furthermore, this literature highlights prominent algorithms and intelligent techniques used for self-healing capabilities and recent advancements in methodologies that offer a comparative analysis to aid researchers and practitioners in selecting appropriate methods.