摘要
针对光伏电池的输出特性受光照强度、温度等因素的影响而具有的非线性特性的问题,为了提高光伏发电系统的发电效率必须对其输出功率进行追踪,并且为了克服MPP追踪过程中收敛速度慢和精度低的缺点,提出了一种RBF-BP组合神经网络对光伏阵列最大功率点追踪的算法。首先通过对光伏电池输出特性的研究,确定了温度和光照强度是影响光伏电池最大功率点输出的主要因素。然后考虑这两个因素作为RBF-BP组合神经网络的输入来设计光伏阵列最大功率点追踪系统。最后,利用Matlab建立该系统的仿真模型,并进行仿真研究与分析。仿真结果表明,该系统具有最大功率点追踪的精度高,响应速度快等优点。从而有效地实现了对光伏最大功率点的追踪,提高了光伏发电系统的发电效率。
The paper presented a tracking algorithm for the maximum power point of photovoltaic arrays based on a RBF - BP neural network. Firstly, through the research on the output characteristics of photovohaic cells, we de- termined that the temperature and light intensity were the main factors affecting the maximum power output of photo- voltaic cells. Then we considered the two factors as the inputs of RBF - BP neural network to design photovohaic ar- ray maximum power point tracking system. Finally, using Matlab, we established the simulation model of the system. The simulation results show that the system has high tracking precision of maximum power point, fast response speed, etc. So it realizes the maximum power power point tracking and improves the efficiency of photovoltaic power genera- tion system.
出处
《计算机仿真》
CSCD
北大核心
2015年第2期131-134,160,共5页
Computer Simulation
基金
江苏省重点科技支撑项目(BE2013005-3)
关键词
光伏电池
最大功率点追踪
神经网络
仿真
Photovoltaic ceils
Maximum power point tracking
Neural network
Simulation
