| 544 | 0 | 10 |
| 下载次数 | 被引频次 | 阅读次数 |
【背景】随着电力巡检需求的日益增长，传统的巡检方式已无法满足现代电网的时效性要求。无人机电力巡检虽具备高效率，但续航能力有限，难以保障远距离杆塔巡检的及时性。【目标】提出一种新型的电力巡检模式，利用多个固定无人机机巢进行巡检，解决无人机续航不足和及时性问题，进一步提升电力巡检效率。【方法】基于总巡检路径最短的优化目标，考虑无人机能耗不确定性、杆塔巡检时间窗以及风速影响，构建结合任务分配与路径规划的组合决策模型。该模型采用改进自适应大邻域搜索算法（ALNS）进行大规模求解，并引入模拟退火准则避免陷入局部最优。【结果】基于实际杆塔数据进行小规模实例分析，将本文算法求解结果与Gurobi求解器结果进行对比，验证了算法的有效性。基于大规模算例结果，改进的ALNS算法能够比传统算法更快速地找到精确解，并显著提高巡检路径规划的效率与准确性，与蚁群算法和ALNS算法相比，改进ALNS在巡检路径距离上分别减少了13.8%和16.7%。DRO模型通过保守的路径规划和任务重分配实现了更均衡的负载分布，有效避免了能耗不确定性导致的任务失败风险。【应用】该巡检方式为电力企业提升巡检效率提供了新的解决思路，并为电力巡检路径规划的优化方案提供了有力的参考与指导。
Abstract：[Background] With the growing demand for power inspection, traditional inspection methods can no longer satisfy the timeliness requirements of modern power grids. Although dronebased power inspections are highly efficient, their limited endurance causes difficulty in guaranteeing the timeliness of long-distance tower inspections. [Objective] A new power inspection model using multiple fixed drone nests is proposed to address the issues of drone endurance and timeliness and further improve the efficiency of power inspections. [Method] A combined decision model was developed, focusing on minimizing the total inspection path length and integrating task allocation and path planning. The model considered the uncertainties in drone energy consumption, tower inspection time windows, and the impact of wind speed. An improved adaptive large neighborhood search(ALNS) algorithm was applied for large-scale solving, and simulated annealing criteria were introduced to avoid local optima. [Results] To validate the performance of the algorithm, the results of the proposed algorithm were compared with those of the Gurobi solver, and practical tower data were used for case analysis, confirming the effectiveness of the algorithm. The improved ALNS algorithm obtained precise solutions faster than traditional algorithms and significantly enhanced the efficiency and accuracy of inspection path planning. Compared with the ant colony optimization algorithm and ALNS, the improved ALNS reduced the inspection path distance by 13.8% and 16.7%, respectively. [Application] This inspection model offers a new solution for power companies to improve inspection efficiency and provides a valuable reference and guidance for optimizing power inspection path planning.
[1] LI Y, GE X, HOU X L. The research of application mode of unmanned aircraft patrol in UHV transmission lines[J].Applied Mechanics and Materials, 2014, 536-537:989-992.
[2] LIU Z, ZHAO X, QI H, et al. Prospect of power inspection using UAV technology[C]//Geoinformatics in Sustainable Ecosystem and Society. Singapore:Springer Singapore, 2020:151-159.
[3]杜茂康,罗娟,李博文.基于多车场的车载无人机协同配送路径优化[J].系统工程, 2021, 39(6):90-98.DU Maokang, LUO Juan, LI Bowen. Research on cooperative delivery route optimization of vehicle-carried drones based on multi-depot[J]. Systems Engineering, 2021, 39(6):90-98.
[4]陈亚青,郑稀元,韩丹,等.民用无人机发展管理现状及路径规划研究进展[J].科学技术与工程, 2022, 22(15):5951-5966.CHEN Yaqing, ZHENG Xiyuan, HAN Dan, et al. Development status and path planning research progress of civil unmanned aerial vehicle[J]. Science Technology and Engineering, 2022, 22(15):5951-5966.
[5]缪希仁,刘志颖,鄢齐晨.无人机输电线路智能巡检技术综述[J].福州大学学报(自然科学版), 2020, 48(2):198-209.MIAO Xiren, LIU Zhiying, YAN Qichen. Review on UAV intelligent technology for transmission line inspection[J]. Journal of Fuzhou University(Natural Science Edition), 2020, 48(2):198-209.
[6] LI Y, YUAN X, ZHU J, et al. Multiobjective scheduling of logistics UAVs based on variable neighborhood search[J]. Applied Sciences, 2020, 10(10):3575.
[7]任璇,黄辉,于少伟,等.车辆与无人机组合配送研究综述[J].控制与决策, 2021, 36(10):2313-2327.REN Xuan, HUANG Hui, YU Shaowei, et al. Review on vehicle-UAV combined delivery problem[J]. Control and Decision, 2021, 36(10):2313-2327.
[8] SONG B D, PARK K, KIM J. Persistent UAV delivery logistics:MILP formulation and efficient heuristic[J]. Computers&Industrial Engineering, 2018, 120:418-428.
[9] YIN F, ZHAO Y. Distributionally robust equilibrious hybrid vehicle routing problem under twofold uncertainty[J]. Information Sciences, 2022, 609:1239-1255.
[10] LI R J, CUI Z, KUO Y H, et al. Scenario-based distributionally robust optimization for the stochastic inventory routing problem[J]. Transportation Research Part E:Logistics and Transportation Review, 2023, 176:103193.
[11] GHOSAL S, WIESEMANN W. The distributionally robust chance-constrained vehicle routing problem[J]. Operations Research, 2020, 68(3):716-732.
[12]曹炳汝,王霞.基于动态需求的多车型车辆配送路径优化研究[J].制造业自动化, 2021, 43(6):79-87.CAO Bingru, WANG Xia. Research on optimization of distribution path of multi-vehicle vehicles based on dynamic demand[J]. Manufacturing Automation, 2021, 43(6):79-87.
[13]王登清.新零售模式下单中心物流配送路径优化研究[J].福州大学学报(自然科学版), 2019, 47(5):695-699.WANG Dengqing. Research on optimizing distribution route of single-center logistics under new retail mode[J].Journal of Fuzhou University(Natural Science Edition),2019, 47(5):695-699.
[14] ESCRIBANO MACIAS J, ANGELOUDIS P, OCHIENG W. Optimal hub selection for rapid medical deliveries using unmanned aerial vehicles[J]. Transportation Research Part C:Emerging Technologies, 2020, 110:56-80.
[15] HU M, LIU W, LU J, et al. On the joint design of routing and scheduling for Vehicle-Assisted Multi-UAV inspection[J]. Future Generation Computer Systems, 2019, 94:214-223.[LinkOut]
[16]苗苗,牛樱清,李晓辉,等.带有禁飞区约束的物流无人机群路径规划[J].兰州大学学报(自然科学版),2023, 59(1):98-105, 111.MIAO Miao, NIU Yingqing, LI Xiaohui, et al. Path planning of unmanned logistic aerial vehicle group under the constraint of a no-fly zone[J]. Journal of Lanzhou University(Natural Sciences), 2023, 59(1):98-105, 111.
[17]曹峰,崔少华,荆治家,等.面向巡检车与无人机协同电力巡检的车辆路径规划方法[J].交通运输工程与信息学报, 2022, 20(2):95-104.CAO Feng, CUI Shaohua, JING Zhijia, et al. Vehicle route planning method for cooperative power inspection by inspection vehicles and drones[J]. Journal of Transportation Engineering and Information, 2022, 20(2):95-104.
[18]黄新林,张隆飛,唐小伟.基于改进遗传算法的家电回收车辆路径规划方法[J].同济大学学报(自然科学版),2024, 52(1):27-34.HUANG Xinlin, ZHANG Longfei, TANG Xiaowei.Path planning method for household appliance recycling vehicle based on improved genetic algorithm[J]. Journal of Tongji University(Natural Science), 2024, 52(1):27-34.
[19]王宏健,鄂鑫,张凯,等.改进蚁群算法解决UUV集群任务规划问题[J].仪器仪表学报, 2022, 43(9):238-254.WANG Hongjian, E Xin, ZHANG Kai, et al. Improved ant colony algorithm to solve UUV cluster task planning problem[J]. Chinese Journal of Scientific Instrument,2022, 43(9):238-254.
[20]伍国华,毛妮,徐彬杰,等.基于自适应大规模邻域搜索算法的多车辆与多无人机协同配送方法[J].控制与决策, 2023, 38(1):201-210.WU Guohua, MAO Ni, XU Binjie, et al. The cooperative delivery of multiple vehicles and multiple drones based on adaptive large neighborhood search[J]. Control and Decision, 2023, 38(1):201-210.
[21]夏小云,庄鹤林,杨火根,等.自适应大邻域搜索的人工蜂群算法求解带容量约束车辆路径问题[J].计算机集成制造系统, 2022, 28(11):3545-3557.XIA Xiaoyun, ZHUANG Helin, YANG Huogen, et al.Adaptive large neighborhood search based artificial bee colony algorithm for CVRP[J]. Computer Integrated Manufacturing Systems, 2022, 28(11):3545-3557.
[22] SUN P, VEELENTURF L P, HEWITT M, et al. Adaptive large neighborhood search for the time-dependent profitable pickup and delivery problem with time windows[J]. Transportation Research Part E:Logistics and Transportation Review, 2020, 138:101942.
[23]南丽君,陈彦如,张宗成.改进的自适应大规模邻域搜索算法求解动态需求的混合车辆路径问题[J].计算机应用研究, 2021, 38(10):2926-2934.NAN Lijun, CHEN Yanru, ZHANG Zongcheng. Improved adaptive large neighborhood search algorithm for mixed fleet routing problem of dynamic demands[J].Application Research of Computers, 2021, 38(10):2926-2934.
[24] GU W J, CATTARUZZA D, OGIER M, et al. Adaptive large neighborhood search for the commodity constrained split delivery VRP[J]. Computers&Operations Research, 2019, 112:104761.
[25]萧理阳,郑航晓,孙鹏,等.基于自适应大规模邻域搜索算法的多等级引航员排班问题[J].交通运输工程与信息学报, 2023, 21(4):149-160.XIAO Liyang, ZHENG Hangxiao, SUN Peng, et al.Multi-level pilot scheduling problem based on adaptive large neighborhood search[J]. Journal of Transportation Engineering and Information, 2023, 21(4):149-160.
[26]刘志锦.基于ALNS算法解决即时配送的车辆路径问题[D].南京：南京大学, 2020.LIU Zhijin. Solving vhicle ruting poblem of istant distribution based on ALNS[D]. Nanjing:Nanjing University, 2020.
[27] DORLING K, HEINRICHS J, MESSIER G G, et al. Vehicle routing problems for drone delivery[J]. IEEE Transactions on Systems, Man, and Gbernetics:Systems, 2016, 47(1):70-85.
[28] WANG W Q, YANG K, YANG L X, et al. Tractable approximations for the distributionally robust conditional vertex p-center problem:application to the location of high-speed railway emergency rescue stations[J]. Journal of the Operational Research Society, 2022, 73(3):525-539.
[29] METROPOLIS N, ROSENBLUTH A W, ROSENBLUTH M N, et al. Equation of state calculations by fast computing machines[J]. The Journal of Chemical Physics, 1953, 21(6):1087-1092.
[30] CROES G A. A method for solving traveling-salesman problems[J]. Operations Research, 1958, 6(6):791-812.
基本信息:
DOI：10.19961/j.cnki.1672-4747.2025.03.038
中图分类号:TM755;TP18
引用信息:
[1]黄祥,吴媚,王海楠等.基于改进ALNS的无人机固定机巢电力巡检路径规划[J].交通运输工程与信息学报,2025,23(03):130-143.DOI:10.19961/j.cnki.1672-4747.2025.03.038.
基金信息: