基于神经网络的光伏系统MPPT控制算法设计

Design of neural network based MPPT control algorithm for photovoltaic system

提出一种基于人工神经网络(ANN)的最大功率点跟踪(MPPT)控制算法。该算法通过扰动和观察(P&O)方法获得人工神经网络模型所需的参数,并分为离线和在线两种模式:离线模式通过测试神经网络参数,找到最佳的网络结构、激活函数和训练算法;在线模式实现优化人工神经网络以便应用于光伏系统。人工神经网络的输入变量为输出功率参数和电压参数,输出变量为归一化的增加或者减少占空比(+1或者-1)。通过Matlab/Simulink模型对所提跟踪算法的性能进行测试验证,结果显示所提算法表现出良好的动态响应速度和稳态控制精度。

A maximum power point tracking(MPPT) control algorithm based on artificial neural network(ANN) is proposed.The algorithm can obtain the parameters needed by the ANN model by means of perturbation and observation(P&O)method.It includes the offline mode and online mode.The former mode can find the optimal network structure,activation function and training algorithm by testing the neural network parameters.The latter mode can optimize the ANN,and apply it to the PV system.The input variables of the ANN are taken as the parameters of the output power and voltage,and the output variable is normalized as the increased or decreased duty ratio(+1 or-1).The performance of the proposed tracking algorithm is tested with Matlab/Simulink model for verification.The results show that the algorithm has perfect dynamic response speed and high steady-state control accuracy.