摘要
局部阴影条件下,光伏发电系统中P-U曲线会呈现多峰现象,传统的最大功率点跟踪(Maximum Power Point Tracking,MPPT)算法易失效,粒子群(PSO)算法适用于复杂多极值系统的寻优,因而在多峰全局MPPT中得到应用。针对寻优过程中传统PSO算法搜索精度低以及易出现早熟现象的缺点,文中提出了自适应惯性权重粒子群(APSO)算法,在PSO算法中引入非线性惯性权重,以提高多峰全局寻优的精度与速度。最后利用MATLAB/Simulink对系统进行仿真,仿真结果表明:在均匀光照和可变阴影条件下,APSO算法能有效提高系统寻优的收敛速度与精度。
Under the condition of local shadow,the P-U curve will show multi-peak phenomenon in photovoltaic power generation system,and the traditional maximum power point tracking(MPPT)algorithm is easy to fail.The particle swarm optimization(PSO)algorithm is suitable for the optimization of complex multi-extremum systems,so it is applied in multi-peak global MPPT.Aiming at the shortcomings of low search precision and premature phenomenon of the traditional PSO algorithm in the optimization process,the adaptive inertial weight particle swarm(APSO)algorithm is proposed in this paper.The nonlinear inertia weight is introduced into PSO algorithm to improve the accuracy and speed of multi-peak global optimization.Finally,the system is simulated by MATLAB/Simulink.The simulation results show that the APSO algorithm can effectively improve the convergence speed and accuracy of the system under the condition of uniform light and variable shadow.
作者
李世光
夏杰
李雪杨
高正中
田朔
Li Shiguang;Xia Jie;Li Xueyang;Gao Zhengzhong;Tian Shuo(School of Electrical Engineering and Automation,Shandong University of Science and Technology,Qingdao 266590,Shandong,China)
出处
《电测与仪表》
北大核心
2019年第22期19-24,共6页
Electrical Measurement & Instrumentation
基金
中国博士后科学基金资助项目(2015T80729)
关键词
局部阴影
多峰
最大功率点跟踪
自适应权重
local shadow
multi-peak
maximum power point tracking
adaptive weight
