Ant Colony Optimization Algorithm Based Obstacle Avoidance Planning Method for Intelligent Inspection Route of UAV in Converter Station

The operation performance, safety and reliability of converter station and the whole DC transmission system are closely related to the safe operation of DC field equipment, and also have an important impact on the operation of the whole power system. Applying UAV (unmanned aerial vehicle) to power l...

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Veröffentlicht in:2023 International Conference on Internet of Things, Robotics and Distributed Computing (ICIRDC) S. 627 - 632
Hauptverfasser: Li, Jingxiang, Lai, Hao, Shi, Yanhui, Liu, Yuchao, Yin, Haitao
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 29.12.2023
Schlagworte:
Ant colony optimization
Autonomous aerial vehicles
Converter Station
Inspection
Obstacle Avoidance Planning
Path planning
Planning
Power system stability
Search problems
UAV
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Zusammenfassung:The operation performance, safety and reliability of converter station and the whole DC transmission system are closely related to the safe operation of DC field equipment, and also have an important impact on the operation of the whole power system. Applying UAV (unmanned aerial vehicle) to power line inspection is a new technology in recent decades. In the process of inspection, UAV should keep a safe distance from the live line. Using UAV automatic obstacle avoidance technology, collision accidents can be effectively avoided and the safety level of UAV inspection can be improved. In this paper, an obstacle avoidance planning method based on improved ACO (ant colony optimization) for intelligent inspection route of UAV in converter station is proposed. The number of pheromones left on each UAV track is limited to a certain range, and the pheromones of each node are updated to effectively avoid the stagnation of the algorithm and the excessive concentration of pheromones on a certain track, thus limiting the spread of the algorithm. The research results show that compared with the contrast method, the success rate of the planned path obtained by the proposed method is higher, with the maximum value of 95.949%, and the number of iterations of the optimal path planning is less, which fully proves that the proposed method has a good effect of path planning. Experimental results show that this method has good stability and can effectively achieve global optimization.
DOI:10.1109/ICIRDC62824.2023.00120