基于自适应径向基函数神经网络的光伏MPPT研究

Photovoltaic System MPPT Algorithm Based on Adaptive Radial Basis Function Neural Network

光伏系统在局部遮阴情况下,输出曲线呈现多峰特性.针对传统最大功率控制算法易追踪到局部最大功率点的缺陷,提出一种基于自适应径向基函数(Radial Basis Function,RBF)神经网络的控制算法.该算法以自适应线性算法优化RBF神经网络的扩展常数与权重,克服了传统神经网络算法收敛速度慢、全局寻优差的缺点.在MATLAB/Simulink环境下建立自适应RBF神经网络仿真模型进行验证,结果表明,提出的算法在外界光照、温度发生变化时能准确找到光伏系统的最大功率点,且在收敛精度和收敛时间上均有很大的提升.

The power-voltage characteristic curve of photovoltaic system has multiple peaks under partial shade condition.The traditional maximum power tracking method can easily trace to the local maximum power point.To solve such shortcoming,a photovoltaic system Maximum Power Point Tracking(MPPT)algorithm based on adaptive radial basis function neural network is proposed.The model optimizes the extended constants and weights of RBF neural network with adaptive linear algorithm,which overcomes the shortcomings of traditional neural network algorithm with slow convergence speed and poor global optimization.The simulation of adaptive RBF neural network is carried out in MATLAB/Simulink environment.The results show that the proposed algorithm can accurately find the maximum power point of the photovoltaic system when the external illumination and temperature change.Moreover the convergence accuracy and convergence time are greatly improved.