摘要
目前研究的电力巡检机器人作业调度优化方法的适应度无法满足收敛原则,导致调度优化过程耗费时间过长。为此,基于遗传算法研究了一种新的电力巡检机器人作业调度优化方法。通过分析尺度特征,确定电力巡检机器人运行图像,计算偏移角度。引入遗传算法,利用编码/解码,设计适应度函数、选择、交叉和变异,选择操作的目标是能够在群体中选出优等个体,并逐渐增加群体中的优等个体;在进行迭代时,根据个体的适应程度来选择确定结果为选中还是淘汰,并根据选择结果实现电力巡检机器人作业调度优化。实验结果表明,该文所提方法在不同函数下得到的适应度结果都能够很好地满足收敛原则,且收敛速度和收敛精度都高于传统方法,能够有效缩短作业调度过程所耗费的时间。
The adaptability of the currently studied power inspection robot scheduling optimization method cannot meet the convergence principle,resulting in a long-time consumption in the scheduling optimization process.For this reason,this paper studies a new optimization method of power inspection robot job scheduling based on genetic algorithm.By analyzing the scale characteristics,the operating image of the electric power inspection robot is determined,and the offset angle is calculated.Genetic algorithm is introduced to design adaptability function,selection,crossover and variation by coding/decoding.The goal of selection operation is to select superior individuals in the population and gradually increase superior individuals in the population.During the iteration,the result is selected or eliminated according to the individual's adaptability,and the job scheduling optimization of electric power inspection robot is realized according to the selection result.The experimental results show that the adaptability results of the proposed method under different functions can well meet the convergence principle,and the convergence speed and convergence accuracy are higher than the traditional methods,which can effectively shorten the time spent in the job scheduling process.
作者
张廷锋
陶熠昆
何凛
赵跃东
ZHANG Tingfeng;TAO Yikun;HE Lin;ZHAO Yuedong(Zhejiang Datang International Shaoxin Jiangbin Thermal Power Generation Co.,Ltd.,Shaoxing 312366,Zhejiang,China;Zhejiang University of Science and Technology,Hangzhou 310023,Zhejiang,China)
出处
《电网与清洁能源》
北大核心
2022年第3期68-73,共6页
Power System and Clean Energy
基金
浙江省自然科学基金项目(202098740024)。
关键词
遗传算法
电力巡检机器人
作业调度
调度优化
优化方法
genetic algorithm
electric power inspection robot
job scheduling
scheduling optimization
optimization method
作者简介
张廷锋(1989—),男,本科,工程师,研究方向为自动化;陶熠昆(1985—),男,硕士,高级工程师,研究方向为自动化控制技术;何凛(1994—),男,本科,助理工程师,研究方向为自动化;赵跃东(1981—),男,本科,工程师,研究方向为发电厂运行管理。
