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<p>Power networks are highly vulnerable to disruptions caused by natural and man-made disasters, necessitating prompt restoration of damaged power supply. This research addresses the challenge of efficiently restoring large-scale power networks, which often involve numerous unknown or uninspected faulty nodes. Leveraging advancements in unmanned aerial vehicles (UAVs) technology, this study facilitates the inspection of these nodes and subsequent manual maintenance. However, coordinating maintenance teams and UAVs is complex due to the intricate network structure and scheduling correlations. We propose a learning-driven (LD) algorithm to enhance human-UAV collaboration for effective power network restoration. The algorithm includes an initialization method to generate promising initial solutions, followed by the use of search operators as basic action elements and a learning engine to guide search directions based on state assessments. Comprehensive experiments validate the algorithm’s effectiveness in improving the restoration process.</p>

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A learning-driven algorithm for maintenance team and UAV collaboration in restoring power network

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月2日

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  • Tiejun Pan,
  • Leina Zheng,
  • Ying Xu,
  • Xuefeng Zhang,
  • Caiming Zhong,
  • Zhang Wang

摘要

Power networks are highly vulnerable to disruptions caused by natural and man-made disasters, necessitating prompt restoration of damaged power supply. This research addresses the challenge of efficiently restoring large-scale power networks, which often involve numerous unknown or uninspected faulty nodes. Leveraging advancements in unmanned aerial vehicles (UAVs) technology, this study facilitates the inspection of these nodes and subsequent manual maintenance. However, coordinating maintenance teams and UAVs is complex due to the intricate network structure and scheduling correlations. We propose a learning-driven (LD) algorithm to enhance human-UAV collaboration for effective power network restoration. The algorithm includes an initialization method to generate promising initial solutions, followed by the use of search operators as basic action elements and a learning engine to guide search directions based on state assessments. Comprehensive experiments validate the algorithm’s effectiveness in improving the restoration process.