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

Tiejun Pan1, Leina Zheng2, Ying Xu3

  • 1School of Information Engineering, College of Science & Technology Ningbo University, No. 521 Wenwei Rd. Baisha Road St., Cixi, Zhejiang Province, 325060, China.

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This study introduces a learning-driven algorithm to improve power network restoration after disasters. It enhances collaboration between human teams and unmanned aerial vehicles (UAVs) for faster, more efficient repairs.

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Area of Science:

  • Electrical Engineering
  • Computer Science
  • Artificial Intelligence

Background:

  • Power grids are vulnerable to disasters, requiring rapid restoration.
  • Large-scale network restoration is challenging due to unknown faulty nodes.
  • Unmanned aerial vehicles (UAVs) can aid in node inspection and maintenance.

Purpose of the Study:

  • To develop an efficient algorithm for large-scale power network restoration.
  • To enhance human-UAV collaboration in disaster recovery operations.
  • To address the complexities of coordinating maintenance teams and UAVs.

Main Methods:

  • Proposed a learning-driven (LD) algorithm for power network restoration.
  • Included an initialization method for generating initial solutions.
  • Utilized search operators and a learning engine for guided search direction.

Main Results:

  • The LD algorithm demonstrated effectiveness in improving power network restoration.
  • Validated through comprehensive experiments, showing enhanced human-UAV collaboration.
  • Successfully addressed complexities in network structure and scheduling correlations.

Conclusions:

  • The LD algorithm offers a promising approach to enhance power grid resilience.
  • Effective human-UAV collaboration is crucial for efficient disaster recovery.
  • This research contributes to faster and more reliable power supply restoration.