摘要
针对传统的扰动观察法在光伏最大功率点跟踪(Maximum Power Point Tracking,MPPT)控制中存在着响应速度慢,难以在最大功率点保持平稳等问题,提出了一种假设法并对传统的粒子群算法提出一种改变惯性权重、学习因子的自适应粒子群算法来实现全局最大功率点跟踪。假设法主要是通过公式假设出最大功率点,基于最大功率点位置进行步长的改进。IPSO算法主要是调整传统粒子群算法的参数、优化粒子的搜索顺序、减少迭代次数。通过MATLAB/SIMULINK软件对其建模仿真,得到了假设法还有IPSO算法的仿真结果,并与传统的算法作了对比。结果表明,采用假设法还有IPSO算法都能够实现光伏最大功率点跟踪的精确控制,有助于光伏系统最大功率点跟踪技术的快速实现,具有较好的应用前景。
Aiming at the problems of the traditional disturbance observation method in photovoltaic MPPT control,such as slow response speed and difficulty in maintaining stability at the maximum power point,a hypothesis method was proposed and an adaptive particle swarm optimization algorithm with changing inertia weight and learning factor was proposed for the traditional particle swarm optimization algorithm to achieve global maximum power point tracking.The assumption method mainly assumed the maximum power point through the formula,and improved the step size based on the position of the maximum power point.IPSO algorithm mainly adjusted the parameters of traditional particle swarm optimization algorithm,optimized the search order of particles,and reduced the number of iterations.Through the modeling and simulation of MATLAB/SIMULINK software,the simulation results of the hypothesis method and IPSO algorithm were obtained and compared with the traditional algorithm.The results showed that both the hypothetical method and IPSO algorithm could achieve the accurate control of PV MPPT,which was helpful for the rapid realization of PV MPPT technology and had a good application prospect.
作者
张崇
王玉峰
ZHANG Chong;WANG Yufeng(School of Electronics and Information Engineering,Liaoning University of Science and Technology,Anshan Liaoning 114000,CHN)
出处
《光电子技术》
CAS
2022年第4期303-310,共8页
Optoelectronic Technology
关键词
光伏发电
假设法
自适应粒子群算法
自适应参数
photovoltaic power generation
prediction method
IPSO algorithm
adaptive parameter
