基于RBF神经网络的风光互补路灯系统MPPT研究

MPPT Study of Wind-PV Hybrid Streetlight System Based on RBF Neural Network Algorithm

为了提高风光互补路灯的稳定性和使用寿命,根据风光互补路灯系统非线性、多物理量等特性,分别对风力发电和光伏发电采取最大功率点跟踪(MPPT)控制。该方法基于径向基函数(RBF)神经网络,分别以风力发电整流器输出的电压、电流和太阳能电池板输出的电压、电流作为RBF神经网络的输入,通过RBF神经网络直接改变Boost电路的占空比,使风光互补系统工作在最大功率点。仿真和试验结果表明,所提出的MPPT算法与扰动观察法算法相比,有更好的快速性和能量利用效率。

In order to improve the stability and service life of the wind-PV hybrid streetlight,according to the nonlinear and multi physical characteristics of the wind-PV hybrid streetlight system,the maximum power point tracking(MPPT) control is adopted for wind power and photovoltaic power respectively. The method is based on radial basis function(RBF) neural network,which takes the voltage and current of wind power rectifier and solar panel as the input of RBF neural network,and directly changes the duty cycle of Boost circuit through RBF neural network,so that the wind solar complementary system works at the maximum power point. Simulation and experimental results show that the provided MPPT algorithm has better speediness and energy efficiency than the disturbance observation algorithm.