考虑容量约束的输电线路巡检无人机机巢选址定型研究

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Abstract Drones are playing an increasingly critical role in transmission line inspection, and the scientific layout of their supporting drone nests directly affects inspection efficiency and investment returns. This paper proposes a site selection and finalization model for drone nests used in transmission line inspection that incorporates capacity constraints. Under a “multi-nest relay” battery-swap mode, the model adopts a multi-objective function aiming to maximize the coverage rate of inspected transmission towers, minimize the total lifecycle cost of construction and operation & maintenance of drone nests, and minimize the total number of deployed nests. Furthermore, the drone’s battery endurance is modeled as a random variable, and operational uncertainties are captured through multi-scenario sampling. Numerical case studies and algorithmic comparisons demonstrate the effectiveness and superiority of the proposed model, offering both theoretical support and a practical solution to the drone nest site selection and finalization problem.

Key words： power transmission inspection drone nest site selection and finalization unmanned aerial vehicle (UAV) robust optimization genetic algorithm capacity constraint

Received: 26 December 2025

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	SU Xiaoyun

Cite this article:

SU Xiaoyun. Research on the selection and finalization of drone nest locations for transmission line inspection considering capacity constraints[J]. Electrical Engineering, 2026, 27(7): 31-36.

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https://dqjs.cesmedia.cn/EN/Y2026/V27/I7/31

[1] 高凯涛, 王雪, 胡萌珊, 等. 以国际视野看中国低空经济经验借鉴与未来机遇[J]. 通信世界, 2025(6): 34-37.
[2] 戴永东, 黄政, 高超, 等. 多目标优化最低代价无人机机巢选址方法研究[J]. 重庆大学学报, 2023, 46(6): 136-144.
[3] 祝一帆, 王强, 项兴尧, 等. 输电线路无人机智能巡检技术概述[J]. 电气开关, 2021, 59(2): 1-3, 6.
[4] 刘向实, 王凌纤, 吴炎彬, 等. 计及配电网运行风险的分布式电源选址定容规划[J]. 电工技术学报, 2019, 34(增刊1): 264-271.
[5] 刘苗苗. 中国低空经济发展模式探索[J]. 中国工程咨询, 2025(5): 89-94.
[6] 麦俊佳. 输电线路巡检无人机巢配置部署与作业调度优化研究[D]. 广州: 华南理工大学, 2024.
[7] 普子恒, 张隆, 余欣芸, 等. 500 kV换流变压器无人机巡检路径规划[J]. 电工技术学报, 2023, 38(增刊1): 204-213.
[8] 胡智敏, 李凯, 汤国锋, 等. 一种输电线路无人机“巢-巢”巡检新模式[J]. 江西电力, 2018, 42(12): 13-15, 25.
[9] Mozaffari M, Saad W, Bennis M, et al.Efficient deployment of multiple unmanned aerial vehicles for optimal wireless coverage[J]. IEEE Communications Letters, 2016, 20(8): 1647-1650.
[10] Chauhan D, Unnikrishnan A, Figliozzi M.Maximum coverage capacitated facility location problem with range constrained drones[J]. Transportation Research Part C: Emerging Technologies, 2019, 99: 1-18.
[11] 叶深文. 道路巡检的无人机机场选址与应急任务调度[D]. 广州: 广东工业大学, 2024.
[12] Chauhan D R, Unnikrishnan A, Figliozzi M, et al.Robust maximum coverage facility location problem with drones considering uncertainties in battery avai- lability and consumption[J]. Transportation Research Record: Journal of the Transportation Research Board, 2021, 2675(2): 25-39.
[13] 刘芳正, 马博闻, 吕博枫, 等. 一种面向移动边缘计算的无人机基站部署方法[J]. 计算机科学, 2022, 49(增刊2): 836-842.
[14] 于惠钧, 马凡烁, 陈刚, 等. 基于改进灰狼优化算法的含光伏配电网动态无功优化[J]. 电气技术, 2024, 25(4): 7-15, 58.
[15] 宋欣杰, 金一鸣. 基于深度学习的架空输电线路绝缘子识别方法研究[J]. 电气技术, 2025, 26(9): 62-68, 78.
[16] 杨帆, 胡源, 张梁, 等. 考虑孤岛时间不确定性的配电网分布式储能选址定容[J]. 电力科学与技术学报, 2023, 38(1): 43-54.
[17] 王永明. 基于多空间约束的含风电电力系统调度鲁棒模型研究[J]. 电气技术, 2018, 19(9): 37-40, 45.
[18] 刘传洋, 吴一全, 刘景景. 无人机航拍图像中绝缘子缺陷检测的深度学习方法研究进展[J]. 电工技术学报, 2025, 40(9): 2897-2916.
[19] 麦俊佳, 崔巍, 曾懿辉. 输电线路巡检无人机巢配置部署方法[J]. 广东电力, 2023, 36(2): 102-108.
[20] 罗忠涛, 罗瑞, 齐浩楠, 等. 基于改进Transformer的天波超视距雷达目标跟踪方法[J]. 信号处理, 2025, 41(11): 1775-1787.
[21] 郭志鸿. 多无人机协同的实时任务分配与航迹规划研究[D]. 重庆: 重庆理工大学, 2022.