考虑风电场尾流的无人机智能巡检路径规划方法OA

[Objectives]Unmanned and intelligent high-efficiency inspection is of great significance for improving the operational reliability of wind turbines and reducing operation and maintenance costs.To address the severe challenges posed by strong turbulence in turbine wakes during operation and the complex multi-obstacle environment in mountainous wind farms,an intelligent unmanned aerial vehicle(UAV)inspection path planning method considering wind farm wake effects is proposed.[Methods]First,a global inspection problem model is established with inspection path cost and UAV endurance time as optimization objectives,and UAV flight characteristics as constraints.A virtual inspection scenario is then designed to reflect the complex terrain and wake characteristics of a mountainous wind farm.Next,a policy interactive target bias rapidly-exploring random trees(PITB-RRT)algorithm is proposed to improve path efficiency and computational efficiency by optimizing sampling and extension strategies.Finally,the non-dominated sorting genetic algorithm Ⅱ(NSGA-Ⅱ)is introduced to optimize the sequence of inspection points.[Results]For a typical mountainous wind farm with 12 wind turbines,the proposed method efficiently plans 66 feasible paths between turbine inspection points.The globally optimal inspection path obtained has a total length of approximately 9 342 meters and a corresponding flight time of 25.3 minutes,reducing the path cost by 28.8%compared to traditional inspection sequences.[Conclusions]The proposed method exhibits good applicability and high efficiency in complex wind farm scenarios,providing technical support for the intelligent and automated execution of UAV inspection tasks.