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光伏阵列多峰最大功率点跟踪研究
引用本文:崔岩,白静晶.光伏阵列多峰最大功率点跟踪研究[J].电机与控制学报,2012,16(6):87-91.
作者姓名:崔岩  白静晶
作者单位:汕头大学工学院,广东汕头,515063
基金项目:广东省科技计划项目,汕头市科技计划项目,汕头大学科研启动基金
摘    要:针对光伏组件输出特性的非线性以及受外部环境的影响,提出了一种遗传神经网络跟踪光伏阵列最大功率点方法.利用太阳能电池的物理特性和输出特性,建立了串联光伏组件的数学模型,给出了组件的电流电压和功率电压特性方程;同时利用MATLAB软件进行仿真、训练及测试,利用遗传神经网络算法成功地对系统最大功率进行跟踪.仿真结果表明,光照强度、温度及遮挡率直接影响着系统最大功率的追踪,与传统的BP算法相比,该算法跟踪的时间更短,精确度更高.

关 键 词:光照不均匀  光伏阵列  遗传神经网络

Research on multi-peak PV module maximum power point tracking
CUI Yan , BAI Jing-jing.Research on multi-peak PV module maximum power point tracking[J].Electric Machines and Control,2012,16(6):87-91.
Authors:CUI Yan  BAI Jing-jing
Affiliation:(College of Engineering,Shantou University,Shantou 515063,China)
Abstract:Aiming at nonlinear output characteristics of PV modules,as well as external factors,an algorithm of PV array maximum power point tracking was presented based on genetic neural network.With the physical characteristics and output characteristics of solar cells,a series of mathematical model of the PV modules was established and the component of current and power characteristic equation was given based on the voltage.Using MATLAB software for simulation,training,testing and genetic algorithm and neural network,the system maximum power was tracked successfully.The simulation results show that the light intensity,temperature and shading rate directly affects the maximum power tracking.Compare with traditional BP algorithm,the algorithm boasts a shorter time track and higher accuracy.
Keywords:non-uniform insolation  Photovoltaic array  genetic algorithm and back propagation neural network
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