摘要
光伏发电系统运行在局部阴影条件下,其P-V曲线呈现多个峰值,为了保证光伏发电系统能够工作在最大功率点下,提出一种改进粒子群优化(PSO)算法的最大功率点跟踪(MPPT)的控制策略。改进算法采用非线性自适应的学习因子和加速因子同时采用随机惯性权重。仿真实验和真实实验验证所提出的算法在光伏发电系统的MPPT控制中具有较快的收敛速度的同时具有较高的精准度和较小的搜索振荡。
The photovoltaic(PV)power generation system operates under local shadow conditions.The PV curve of the PV power generation system presents multiple peaks.In order to ensure that the PV power generation system can work at the maximum power point,a control strategy for improving the maximum power point tracking(MPPT)of the particle swarm optimization(PSO)algorithm is proposed.The improved algorithm adopts nonlinear adaptive learning factor and acceleration factor and adopts random inertia weight.Simulation experiment and the real experiment are carried out to verify that the proposed algorithm has a faster convergence speed in the MPPT control of the PV power generation system,and has higher precision and smaller search oscillation.
作者
孙恺
刘光宇
SUN Kai;LIU Guangyu(School of Automation Engineering,Hangzhou Dianzi University,Hangzhou 310000,China)
出处
《传感器与微系统》
CSCD
北大核心
2021年第1期49-52,共4页
Transducer and Microsystem Technologies
基金
国家自然科学基金资助项目(61174074)
国家重大科研仪器研制资助项目(61427808)。
