海上风电场风机叶片巡检路径规划算法研究分析  

Research and Analysis of Path Planning Algorithm for Aind Turbine Blade Inspection in Offshore Wind Farms

作　　者：陈丽城 申毅莉[1] 邓鹰飞[1] 姚金[1] CHEN Licheng;SHEN Yili;DENG Yingfei;YAO Jin(School of Mechanical and Resource Engineering,Wuzhou University,Wuzhou 543002,Guangxi,China)

机构地区：[1]梧州学院机械与资源工程学院,广西梧州543002

出　　处：《长江信息通信》2025年第1期99-101,共3页Changjiang Information & Communications

基　　金：2022年度广西高校中青年教师基础能力提升项目:海上风电场风机叶片缺陷识别关键技术研究项目资助(项目编号:2022KY0669)。

摘　　要：采用无人机巡检,是目前海上风电场风机叶片巡检的主流方式,但是无人机巡检过程中的续航问题,是巡检中的一个关键。文章提出通过优化巡检路径,即在无人机续航能力有限的客观下,通过巡检路径优化,使无人机到达各点的路径最短。文章重点研究了Hopfield神经网络算法在风机叶片巡检路径优化中的算法原理及应用分析,并在Matlab上进行了算法仿真验证及分析。仿真结果表明算法可行,达到了巡检路线最优的效果,实现巡检效率最高。The use of drone inspection is currently the mainstream method for inspecting wind turbine blades in offshore wind farms,but the endurance issue during drone inspection is a key issue in the inspection process.This article proposes to optimize the inspection path,which aims to minimize the path for the drone to reach various points by optimizing the inspection path under the objective condition of limited endurance of the drone.The article focuses on the algorithm principle and application analysis of Hopfield neural network algorithm in optimizing the inspection path of wind turbine blades,and conducts algorithm simulation verification and analysis on Matlab.The simulation results show that the algorithm is feasible,achieving the optimal effect of inspection routes and achieving the highest inspection efficiency.

关 键 词：路径规划 HOPFIELD神经网络 叶片巡检 海上风电 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]