摘要
由于光伏阵列电压和电流的非线性,光伏发电输出能量存在最大功率点.为提高光伏发电系统的发电效率,提出了一种基于神经网络和Cuk变换器对光伏阵列最大功率点跟踪的算法.神经网络输入变量为温度和光照强度,学习算法采用梯度下降法,输出量为电压信号,用于调节Cuk变换器的开关占空比.仿真结果表明,该算法最大功率点跟踪控制精度较高,响应迅速,且系统适应性良好.
Because of the photovoltaic array voltage and current are non-linear,there is a maximum power point of PV output energy.To improve the efficiency of electricity generating systems of PV,on the basis of the the neural network and Cuk converters,a tracking algorithm is presented for the maximum power point of photovoltatic arrays.Input variables of neural network are temperature and irradiance.Learning algorithm uses Gradient descent learning algorithm.Output is a voltage signal for regulating the Cuk converter switch duty ratio.Simulation results show that this method for the maximum power point tracking control can achieve relatively high precision error,fast response,and the system adaptability is good.
出处
《华北水利水电学院学报》
2010年第6期80-83,共4页
North China Institute of Water Conservancy and Hydroelectric Power
关键词
光伏发电
最大功率点跟踪
神经网络
photovoltaic power generation
the maximum power point tracking
neural network
