考虑风电场尾流的无人机智能巡检路径规划方法

发电技术

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考虑风电场尾流的无人机智能巡检路径规划方法

柳旭¹，莫皓添²，闫翔昱³，方健豪^2*，陆春波¹，范佳¹，胡伟飞²

1．华电(宁夏)能源有限公司新能源分公司，宁夏回族自治区银川市 750000；2．浙江大学机械工程学院，浙江省杭州市 310058；3.华电电力科学研究院有限公司，浙江省杭州市 310030

基金资助:
国家自然科学基金项目(52275275)；浙江省“尖兵领雁”项目(2023C01008)

Path Planning Method for UAV Intelligent Inspection Considering Wind Farm Wake

LIU Xu¹, MO Haotian², YAN Xiangyu³, FANG Jianhao^2*, LU Chunbo¹, FAN Jia¹, HU Weifei²

1. New Energy Branch of Huadian (Ningxia) Energy Co., Ltd., Yinchuan 750000, Ningxia Autonomous Region, China； 2. College of Mechanical Engineering, Zhejiang University, Hangzhou 310058, Zhejiang Province, China； 3. Huadian Electric Power Research Institute Co., Ltd., Hangzhou 310030, Zhejiang Province, China

Supported by:
National Natural Science Foundation of China (52275275); Zhejiang Province “Leading Geese” Project (2023C01008)

摘要/Abstract

摘要： 【目的】无人化、智能化的高效巡检对于提升风力发电机运行过程可靠性、降低运维成本具有重大意义。针对风机运行过程中尾流的强湍流环境，以及山地风电场的多障碍物复杂环境对无人机巡检路径规划提出的严峻挑战，提出了一种考虑风电场尾流的无人机智能巡检路径规划方法。【方法】首先，构建了以巡检路径成本与无人机续航时间为优化目标、无人机飞行特性为约束条件的全局巡检问题模型，并设计了一个反映山地风电场复杂地形和风机尾流特征的虚拟巡检场景。其次，提出了一种基于策略交互的目标偏置快速随机树(policy interactive target bias rapidly-exploring random trees，PITB-RRT)算法，通过优化采样与扩展策略提升路径效率与计算效率。最后，引入非支配排序遗传算法Ⅱ型(non-dominated sorting genetic algorithm Ⅱ，NSGA-Ⅱ)优化巡检点次序。【结果】针对一个包含12台风机的典型山地风电场，所提方法高效规划出66条风机巡检点间的可行路径，求解的全局最优巡检路径长度约9 342 m，对应飞行时间为25.3 min，与传统巡检次序相比，路径成本降低了28.8%。【结论】所提方法在复杂风电场景中具有良好的适用性和高效性，为无人机巡检任务的智能化与自动化提供了技术支持。

关键词: 风力发电, 风机, 风电场, 智能巡检, 无人机(UAV), 路径规划, 快速搜索随机树(RRT)

Abstract: [Objectives] Unmanned and intelligent efficient inspections is of significant importance for enhancing the reliability of wind turbine operation and reducing maintenance costs. Addressing the severe challenges posed by the strong turbulent environment of wake flows during turbine operation and the complex multi-obstacle environment of mountainous wind farms for unmanned aerial vehicle (UAV) inspection path planning, this paper proposes an intelligent UAV inspection path planning method that considers wind farm wake effects. [Methods] Firstly, a global inspection problem model was constructed, with the optimization objectives of inspection path cost and drone endurance time, and drone flight characteristics as constraint conditions. A virtual inspection scenario reflecting the complex terrain of mountainous wind farms and the characteristics of turbine wake flows is designed. Secondly, a policy interactive target bias rapidly-exploring random trees (PITB-RRT) algorithm is proposed, which significantly improves path efficiency and computational efficiency by optimizing sampling and expansion strategies. Additionally, 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 comprising 12 turbines, the method efficiently plans 66 feasible paths between inspection points. The solved optimal globally inspection path has a length of approximately 9 342 m, corresponding to a flight time of 25.3 minutes, reducing path cost by 28.8% compared to traditional sequences. [Conclusions] The proposed method exhibits excellent applicability and efficiency in complex wind farm scenarios, providing technical support for the intelligentization and automation of UAV inspection tasks.

Key words: wind power, wind turbine, wind farm, intelligent inspection, unmanned aerial vehicle (UAV), path planning, rapidly-exploring random trees

柳旭, 莫皓添, 闫翔昱, 方健豪, 陆春波, 范佳, 胡伟飞. 考虑风电场尾流的无人机智能巡检路径规划方法[J]. 发电技术.

LIU Xu, MO Haotian, YAN Xiangyu, FANG Jianhao, LU Chunbo, FAN Jia, HU Weifei. Path Planning Method for UAV Intelligent Inspection Considering Wind Farm Wake[J]. Power Generation Technology.

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