求解变电站巡视路径问题的改进粒子群算法研究
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摘要
随着电网设备规模日益扩大,利用智能机器人巡视变电站设备的技术得以广泛应用。而优化变电站设备巡视路径是降低智能巡视机器人能耗,提高机器人工作效率的有效手段。提出了一种改进粒子群算法,通过引进进化变异粒子个体,并且对惯性权重进行自适应调整,实现了粒子种群的动态更新。既解决了传统算法的早期容易收敛于局部最优解的问题,又能在运算的后期快速求得全局最优解,提高计算速度,并通过Matlab仿真应用于优化某500 kV变电站设备巡视路径,计算结果证明了该算法能高效求得最优路径。
With the increasing scale of power grid equipment,the technology of using intelligent robot to patrol substation equipment has been widely used.Optimizing the circuit path of substation equipment is an effective means to reduce energy consumption of intelligent patrol robot and improve its working efficiency.An improved particle swarm optimization algorithm is proposed,which can dynamically update the population of particles by introducing the evolutionary variation of individual particles and adjusting the inertia weight adaptively.It not only solves the problem that the traditional algorithm is easy to converge to the local optimal solution in the early stage,but also can quickly obtain the global optimal solution in the late stage of the operation and improve the calculation speed,then the Matlab simulation is applied to optimize the patrol path of a 500 kV substation equipment.The calculation results prove that the algorithm an efficiently obtain the optimal path.
With the increasing scale of power grid equipment,the technology of using intelligent robot to patrol substation equipment has been widely used.Optimizing the circuit path of substation equipment is an effective means to reduce energy consumption of intelligent patrol robot and improve its working efficiency.An improved particle swarm optimization algorithm is proposed,which can dynamically update the population of particles by introducing the evolutionary variation of individual particles and adjusting the inertia weight adaptively.It not only solves the problem that the traditional algorithm is easy to converge to the local optimal solution in the early stage,but also can quickly obtain the global optimal solution in the late stage of the operation and improve the calculation speed,then the Matlab simulation is applied to optimize the patrol path of a 500 kV substation equipment.The calculation results prove that the algorithm an efficiently obtain the optimal path.
引文
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[2]郭文忠,陈国龙.求解TSP问题的模糊自适应粒子群算法[J].计算机科学,2006,33(6):161-162.GUO Wenzhong,CHEN Guolong.Fuzzy self adapted particle swarm optimization algorithm for traveling salesman problems[J].Computer Science,2006,33(6):161-162.
[3]EBERHART R C,KENNETY J.A new optimizer using particles swarm theory[C].Sixth International Symposium on Micro Machine and Human Science,1995:39-43.
[4]钱玉良,张浩,彭道刚,等.基于新的广义粒子群方法的发电机组轴心轨迹提纯[J].中国电机工程学报,2012,32(2):130-137.QIAN Yuliang,ZHANG Hao,PENG Daogang,et al.Orbit purification of generator unit based on a new generalized particle swarm optimization method[J].Proceedings of the CSEE,2012,32(2):130-137.
[5]王东,吴湘滨,毛先成,等.一种改进的求解TSP混合粒子群优化算法[J].计算机工程,2008,34(6):185-187.WANG Dong,WU Xiangbin,MAO Xiancheng,et al.Improved hybrid particle swarm optimization algorithm for solving TSP[J].Computer Engineering,2008,34(6):185-187.
[6]熊伟,张江维,张火林.求解TSP问题的增强型自探索粒子群算法[J].华北电力大学学报,2009,36(6):69-74.XIONG Wei,ZHANG Jiangwei,ZHANG Huolin.Enhanced self-tentative particle swarm optimization algorithm for TSP[J].Journal of North China Electric Power University,2009,36(6):69-74.
[7]谢旻.一种混合粒子群优化算法在TSP中的应用[J].太原理工大学学报,2013,44(4):506-509.XIE Min.An improved hybrid particle swarm optimization algorithm for TSP[J].Journal of Taiyuan University of Technology,2013,44(4):506-509.
[8]高海兵,周驰,高亮.广义粒子群优化模型[J].计算机学报,2005,28(12):1980-1987.GAO Haibing,ZHOU Chi,GAO Liang.General particle swarm optimization model[J].Chinese Journal of Computers,2005,28(12):1980-1987.
[9]康朝海,李鹏娜,张永丰,等.基于混合蛙跳粒子群算法的TSP问题求解[J].吉林大学学报,2017,35(5):498-506.KANG Chaohai,LI Pengna,ZHANG Yongfeng,et al.Solving travelling salesman problem based on shuff-led frog leaping algorithm and particle swarm optimization algorithm[J].Journal of Jilin University,2017,35(5):498-506.
[10]PARSOPOULOSKE,VRAHATISMN.Recent approaches to global optimization problems through particle swarm optimization[J].Natural Computing,2002,1(2):235-306.
[11]刘福才,贾亚飞,任丽娜.基于混沌粒子群优化算法的异结构混沌反同步自抗扰控制[J].物理学报,2013,62(12):91-98.LIU Fucai,JIA Yafei,REN Lina.Ant isynchronizing different chaotic systems using active disturbance rejection controller based on the chaos particle swarm optimization algorithm[J].Acta Phgsica Sinica,2013,62(12):91-98.