面向大规模光伏电站的无人机巡检路径规划策略

doi:10.3969/j.issn.2097-0706.2024.11.006

综合智慧能源 ›› 2024, Vol. 46 ›› Issue (11): 46-53.doi: 10.3969/j.issn.2097-0706.2024.11.006

• 人工智能赋能的运维与巡检 • 上一篇下一篇

面向大规模光伏电站的无人机巡检路径规划策略

吴张宇¹^,²(), 吴池莉¹^,², 于慧铭¹^,², 政幸男¹^,²^,^*(), 张啸宇¹^,²

1.安徽大学人工智能学院，合肥 230601
2.自主无人系统技术教育部工程研究中心，合肥 230601

收稿日期:2024-09-18 修回日期:2024-10-19 出版日期:2024-11-25
通讯作者: * 政幸男（2000），男，硕士生，从事无人机智能巡检方面的研究，wa22301079@stu.ahu.edu.cn。
作者简介:吴张宇（2004），男，科研助理，从事无人机控制方面的研究，wa2224184@stu.ahu.edu.cn。
基金资助:
国家自然科学基金项目(62303005);安徽省大学生创新创业训练计划项目(S202410357259);安徽大学校级质量工程项目(2024XJZLGC164);安徽大学校级质量工程项目(2024XJZLGC168)

Path planning strategy of UAV inspection of large-scale photovoltaic power stations

WU Zhangyu¹^,²(), WU Chili¹^,², YU Huiming¹^,², ZHENG Xingnan¹^,²^,^*(), ZHANG Xiaoyu¹^,²

1. School of Artificial Intelligence，Anhui University，Hefei 230601，China
2. Engineering Research Center of Autonomous Unmanned System Technology，Ministry of Education，Hefei 230601，China

Received:2024-09-18 Revised:2024-10-19 Published:2024-11-25
Supported by:
National Natural Science Foundation of China Project(62303005);Anhui Province College Students Innovation and Entrepreneurship Training Program(S202410357259);Anhui University Quality Engineering Project(2024XJZLGC164);Anhui University Quality Engineering Project(2024XJZLGC168)

摘要/Abstract

摘要：

随着光伏产业的发展，以人工巡检方式为主的大规模光伏电站日常运维工作成本日益增长。而无人机（UAV）巡检技术的广泛应用有效降低巡检成本，提升巡检效率。针对大规模光伏电站巡检问题，提出一种基于聚类算法和蚁群算法（ACO）的UAV路径规划方法。基于大规模光伏电站光伏组串的规整布局，考虑UAV视野范围和飞行高度，规划拍照点以覆盖所有组串，并构建拍照点的散点图。考虑UAV的续航，提出一种K-means算法将大规模光伏电站划分为多个巡检子区域。针对多个巡检子区域，提出一种覆盖式精英ACO进行路径规划，覆盖巡检区域内全部光伏组串，有效减少UAV巡检路径长度并降低其能耗。在实际光伏电站（30 MWp）场景中验证了所提出算法的有效性。

关键词: 无人机, 光伏巡检, 区域划分, 路径规划, 蚁群算法

Abstract:

With the development of the photovoltaic industry， daily operation and maintenance costs for large-scale photovoltaic power stations， which mainly rely on manual inspections， are increasing. The widespread application of unmanned aerial vehicle（UAV）inspection technology effectively reduces inspection costs and improves inspection efficiency. To address the inspection challenges of large-scale photovoltaic power stations， a UAV path planning method based on clustering algorithm and ant colony algorithm was proposed. Based on the regular layout of photovoltaic clusters in large-scale photovoltaic power plants， and considering the field of view and flying altitude of drones， photographic points were planned to cover all clusters， and a scatter plot of these points was constructed. Considering the endurance of UAV， a condensed K-means clustering algorithm was proposed to divide the large-scale photovoltaic power station into multiple inspection sub-regions. For multiple inspection sub-regions， a coverage-based elite ant colony algorithm was proposed for path planning， ensuring that all photovoltaic strings within the inspection area were covered， which effectively reduced the inspection path length and energy consumption of UAV. The effectiveness of the proposed algorithm was then validated in a real-world photovoltaic power station （30 MWp） scenario.

Key words: unmanned aerial vehicle, photovoltaic inspection, regional division, path planning, ant colony optimization

中图分类号:

TK01：TM615

吴张宇, 吴池莉, 于慧铭, 政幸男, 张啸宇. 面向大规模光伏电站的无人机巡检路径规划策略[J]. 综合智慧能源, 2024, 46(11): 46-53.

WU Zhangyu, WU Chili, YU Huiming, ZHENG Xingnan, ZHANG Xiaoyu. Path planning strategy of UAV inspection of large-scale photovoltaic power stations[J]. Integrated Intelligent Energy, 2024, 46(11): 46-53.

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项目	聚类指标
项目	轮廓系数	方差比准则	DBI值
K-means	0.452	556.9	0.736
AHC	0.427	446.0	0.823
谱聚类	0.443	539.8	0.776
密度聚类	0.355	314.7	1.332
本文算法	0.452	557.3	0.732

参数	数值
蚂蚁数量	50
最大迭代次数	500
信息素影响权重	1
启发式函数影响权重	5
调整速率	0.1
信息素常量系数	1

项目	路径长度/km	转弯次数	飞行时间/s
贪婪算法	15.39	181	2 987
传统ACO	13.93	153	2 620
遗传算法	35.67	207	5 223
模拟退火算法	31.47	141	4 277
粒子群算法	45.38	288	6 842
本文算法	13.85	134	2 457

面向大规模光伏电站的无人机巡检路径规划策略

Path planning strategy of UAV inspection of large-scale photovoltaic power stations

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