基于改进遗传算法的光伏板清洁分级任务规划

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

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.

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.