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With the normalized application of drones in the power inspection field, fully autonomous inspection based on drone airports has become a key measure to reach the digitalization and intelligentization in power grid operation and maintenance. This article focuses on the optimization of the autonomous inspection system based on the gridded airport configuration, promoting the efficient of the schedule and execution in the inspection tasks through scientific airport location strategies. The penalty function mechanism and dynamic coefficient adjustment strategy are integrated on the particle swarm optimization algorithm framework, and the innovative introduction of neighborhood search and particle resurrection strategies are introduced to enhances the global optimization performance of the algorithm. By adopting the small airport, medium airport, and mixed deployment all airports three strategies, and carrying out the airport location optimization for 2944 overhead towers in 223 square kilometers, the target tower inspection is covered completely. The experiment demonstrates the effectiveness and practicability of the improved algorithm under different deployment strategies, which provides a scientific and practical guidance for the power distribution networks drone airports planning and deployment.
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Yang et al. (2024) studied this question.
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