上海交通大学学报

上海交通大学学报 >

2021 , Vol. 55 >Issue 9: 1169 - 1174

DOI: https://doi.org/10.16183/j.cnki.jsjtu.2020.254

基于改进遗传算法的光伏板清洁分级任务规划

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  • 兰州理工大学 机电工程学院,兰州 730050
李翠明(1976-),女,甘肃省白银市人,副教授,主要从事机器人运动控制与路径规划研究;E-mail: li_goddess@163.com

收稿日期: 2020-08-14

  网络出版日期: 2021-10-08

基金资助

甘肃省自然科学基金(18JR3RA139);甘肃省省级引导科技创新发展项目(2018ZX-13)

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Hierarchical Mission Planning for Cleaning Photovoltaic Panels Based on Improved Genetic Algorithm

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  • School of Mechanical and Electronical Engineering, Lanzhou University of Technology, Lanzhou 730050, China

Received date: 2020-08-14

  Online published: 2021-10-08

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摘要

针对利用移动清洁机器人对大面积光伏电站光伏板清洁作业时的任务规划问题,提出分区规划策略.根据风口、光照时长等环境因素对光伏电站采用基于清洁优先级的分级任务规划,利用Hamilton图将太阳能光伏板清洁问题转化为巡回旅行商问题(TSP).针对遗传算法效率低、容易过早收敛的缺点,提出锦标赛选择法与轮盘赌选择法相结合的混合选择算子和基于分段规则的交叉算子的改进遗传算法.采用改进遗传算法规划机器人清洁光伏电站的清洁顺序.实验结果表明,相比于自适应遗传算法,改进遗传算法的求解效率更高、结果更好.

关键词： 光伏电站; 清洁机器人; 任务规划; 遗传算法; 旅行商问题

本文引用格式

李翠明, 王宁, 张晨 . 基于改进遗传算法的光伏板清洁分级任务规划[J]. 上海交通大学学报, 2021 , 55(9) : 1169 -1174 . DOI: 10.16183/j.cnki.jsjtu.2020.254

Abstract

Aimed at the mission planning for cleaning photovoltaic panels in large-area photovoltaic plants with mobile cleaning robots, a district planning strategy is hereby proposed. The photovoltaic plants, considering the position of wind gaps, the illumination time, and other environmental factors, adopt a hierarchical mission planning based on the cleaning priority, and use the Hamilton graph to turn the cleaning problem of photovoltaic panels into a travelling salesman problem (TSP). Considering the disadvantages of low efficiency and early convergence of the genetic algorithm, an improved genetic algorithm, which includes the hybrid selection operator combining the tournament selection with the roulette wheel selection and the crossover operator based on the segmentation rule is thus put forward. The improved genetic algorithm is applied to plan the cleaning order of robots to clean the photovoltaic panels. The experimental results show that in comparison with the adaptive genetic algorithm, the improved genetic algorithm has a higher efficiency and better results.

Key words： photovoltaic plants; cleaning robot; mission planning; genetic algorithm; travelling salesman problem (TSP)

参考文献

[1] 尤鸿芃, 汪婷婷, 何银涛. 积灰对多晶硅光伏组件效率影响的实验研究[J]. 太阳能, 2013(23):39-41.
[1] YOU Hongpeng, WANG Tingting, HE Yintao. Experimental study on the effect of ash accumulation on the efficiency of polycrystalline silicon photovoltaic modules[J]. Solar Energy, 2013(23):39-41.
[2] 王平, 杜炜, 张海宁, 等. 表面积灰影响光伏组件泄漏电流与衰减寿命的研究[J]. 太阳能学报, 2019, 40(1):119-125.
[2] WANG Ping, DU Wei, ZHANG Haining, et al. Pollution impact on the leakage current and power degradation of photovoltaic modules[J]. Acta Energiae Solaris Sinica, 2019, 40(1):119-125.
[3] 龚芳馨, 刘晓伟, 王靓. 光伏电站太阳能板的清洁技术综述[J]. 水电与新能源, 2015(5):71-73.
[3] GONG Fangxin, LIU Xiaowei, WANG Jing. Discussion on cleaning technology of the PV module in photovoltaic power stations[J]. Hydropower and New Energy, 2015(5):71-73.
[4] 张明明. 基于Kinect2的光伏清洗机器人实时环境重建与自主导航技术研究[D]. 哈尔滨: 哈尔滨工业大学, 2016.
[4] ZHANG Mingming. Research on simutaneous mapping and automated navigation for photovotanic cleaning robot with Kinect2[D]. Harbin: Harbin Institute of Technology, 2016.
[5] 孙卫红, 覃剑戈, 岳云涛. 煤矿塌陷区光伏电站太阳能电池板清洁机器人的研究[J]. 中国煤炭, 2018, 44(4):144-149.
[5] SUN Weihong, QIN Jiange, YUE Yuntao. Study on solar panel cleaning robots of photovoltaic power station in coal mine subsidence areas[J]. China Coal, 2018, 44(4):144-149.
[6] 吴新民. 基于Unity3D的光伏板清洁移动机器人路径规划可视化研究[D]. 兰州: 兰州理工大学, 2020.
[6] WU Xinmin. Visual research on path planning of photovoltaic cleaning mobile robot based on Unity3D[D]. Lanzhou: Lanzhou University of Technology, 2020.
[7] LI Y, MA K, ZHANG J. An efficient multicore based parallel computing approach for TSP problems[C]// 2013 Ninth International Conference on Semantics, Knowledge and Grids. Piscataway, NJ, USA: IEEE, 2013: 98-104.
[8] LIN B, SUN X Y, SALOUS S. Solving travelling salesman problem with an improved hybrid genetic algorithm[J]. Journal of Computer and Communications, 2016, 4(15):98-106.
[9] EBADINEZHAD S. DEACO: Adopting dynamic evaporation strategy to enhance ACO algorithm for the traveling salesman problem[J]. Engineering Applications of Artificial Intelligence, 2020, 92:103649.
[10] OUAARAB A, AHIOD B, YANG X S. Random-key cuckoo search for the travelling salesman problem[J]. Soft Computing, 2015, 19(4):1099-1106.
[11] 吴虎胜, 张凤鸣, 李浩, 等. 求解TSP问题的离散狼群算法[J]. 控制与决策, 2015, 30(10):1861-1867.
[11] WU Husheng, ZHANG Fengming, LI Hao, et al. Discrete wolf pack algorithm for traveling salesman problem[J]. Control and Decision, 2015, 30(10):1861-1867.
[12] 陶泽, 张海涛. 基于遗传算法的Job-Shop调度问题研究[J]. 沈阳理工大学学报, 2016, 35(2):60-64.
[12] TAO Ze, ZHANG Haitao. Studies on job-shop scheduling problems based on genetic algorithms[J]. Journal of Shenyang Ligong University, 2016, 35(2):60-64.
[13] 苏永杰, 胡俊. 基于遗传算法的线束加工仓库货位优化研究[J]. 包装工程, 2018, 39(19):110-116.
[13] SU Yongjie, HU Jun. Optimization for automobile harness processing storage location assignment based on genetic algorithm[J]. Packaging Engineering, 2018, 39(19):110-116.
[14] 阮梦黎. 基于改进GEP的航空器故障数据挖掘研究与仿真[J]. 计算机仿真, 2015, 32(6):92-95.
[14] RUAN Mengli. Simulation of aircraft fault data mining algorithm based on mew GEP[J]. Computer Simulation, 2015, 32(6):92-95.
[15] CERF R. The quasispecies regime for the simple genetic algorithm with roulette wheel selection[J]. Advances in Applied Probability, 2017, 49(3):903-926.
[16] VAHDATI G, YAGHOUBI M, POOSTCHI M, et al. A new approach to solve traveling salesman problem using genetic algorithm based on heuristic crossover and mutation operator[C]// 2009 International Conference of Soft Computing and Pattern Recognition. Piscataway, NJ, USA: IEEE, 2009: 112-116.
[17] 李志雄. 基于ROS的移动机器人导航技术研究[D]. 绵阳: 西南科技大学, 2016.
[17] LI Zhixiong. Research on mobile robot navigation technology based on ROS[D]. Mianyang: Southwest University of Science and Technology, 2016.
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