光伏阵列在局部遮荫情况下,P-U曲线呈现多峰值特性,传统的最大功率点跟踪(maximum power point tracking,MPPT)算法收敛速度快但易陷入局部最优,元启发式算法收敛速度慢但适用于多极值寻优问题,因此,本文提出一种改进蛇形算法(improved ...光伏阵列在局部遮荫情况下,P-U曲线呈现多峰值特性,传统的最大功率点跟踪(maximum power point tracking,MPPT)算法收敛速度快但易陷入局部最优,元启发式算法收敛速度慢但适用于多极值寻优问题,因此,本文提出一种改进蛇形算法(improved snake optimization,ISO)结合电导增量法(incremental conductance,INC)的复合算法。首先,该算法利用ISO进行全局寻优,到达最大功率点附近后再利用INC进行精细搜索,最后,利用变步长INC的快速收敛性提高算法的寻优速度和精度。使用Matlab/Simulink仿真软件,构建了一个在局部阴影条件下的光伏发电系统模型,并在相同的种群规模下,对ISO、蝴蝶算法(butterfly optimization algorithm,BOA)进行了测试和比较,从而验证了所提算法的有效性。展开更多
Using the traditional swarm intelligence algorithm to solve the cooperative path planning problem for multi-UAVs is easy to incur the problems of local optimization and a slow convergence rate.A cooperative path plann...Using the traditional swarm intelligence algorithm to solve the cooperative path planning problem for multi-UAVs is easy to incur the problems of local optimization and a slow convergence rate.A cooperative path planning method for multi-UAVs based on the improved sheep optimization is proposed to tackle these.Firstly,based on the three-dimensional planning space,a multi-UAV cooperative cost function model is established according to the path planning requirements,and an initial track set is constructed by combining multiple-population ideas.Then an improved sheep optimization is proposed and used to solve the path planning problem and obtain multiple cooperative paths.The simulation results show that the sheep optimization can meet the requirements of path planning and realize the cooperative path planning of multi-UAVs.Compared with grey wolf optimizer(GWO),improved gray wolf optimizer(IGWO),chaotic gray wolf optimizer(CGWO),differential evolution(DE)algorithm,and particle swam optimization(PSO),the convergence speed and search accuracy of the improved sheep optimization are significantly improved.展开更多
文摘光伏阵列在局部遮荫情况下,P-U曲线呈现多峰值特性,传统的最大功率点跟踪(maximum power point tracking,MPPT)算法收敛速度快但易陷入局部最优,元启发式算法收敛速度慢但适用于多极值寻优问题,因此,本文提出一种改进蛇形算法(improved snake optimization,ISO)结合电导增量法(incremental conductance,INC)的复合算法。首先,该算法利用ISO进行全局寻优,到达最大功率点附近后再利用INC进行精细搜索,最后,利用变步长INC的快速收敛性提高算法的寻优速度和精度。使用Matlab/Simulink仿真软件,构建了一个在局部阴影条件下的光伏发电系统模型,并在相同的种群规模下,对ISO、蝴蝶算法(butterfly optimization algorithm,BOA)进行了测试和比较,从而验证了所提算法的有效性。
基金supported in part by the Fundamental Research Funds for the Central Universities(No.NZ18008)。
文摘Using the traditional swarm intelligence algorithm to solve the cooperative path planning problem for multi-UAVs is easy to incur the problems of local optimization and a slow convergence rate.A cooperative path planning method for multi-UAVs based on the improved sheep optimization is proposed to tackle these.Firstly,based on the three-dimensional planning space,a multi-UAV cooperative cost function model is established according to the path planning requirements,and an initial track set is constructed by combining multiple-population ideas.Then an improved sheep optimization is proposed and used to solve the path planning problem and obtain multiple cooperative paths.The simulation results show that the sheep optimization can meet the requirements of path planning and realize the cooperative path planning of multi-UAVs.Compared with grey wolf optimizer(GWO),improved gray wolf optimizer(IGWO),chaotic gray wolf optimizer(CGWO),differential evolution(DE)algorithm,and particle swam optimization(PSO),the convergence speed and search accuracy of the improved sheep optimization are significantly improved.
