基于T-S模型的模糊神经网络光伏MPPT控制

A Photovoltaic Maximum Power Point Tracking Control Based on Fuzzy Neural Network of T-S Model

为了克服传统最大功率点跟踪(MPPT)方法的一些缺点,使光伏系统更加快速准确地工作在最大功率输出点,提出了基于模糊控制和神经网络控制相结合的自适应控制方法。该方法充分利用模糊神经网络处理非线性问题的优点,通过模糊控制来改变步长,利用神经网络的自学习能力来快速达到平衡,使光伏MPPT在跟踪速度和稳定性之间达到一个较优的平衡。仿真和试验结果表明,基于模糊神经网络自适应控制的MPPT方法具有较强的鲁棒性和自适应能力。

In order to overcome some shortcomings of the traditional maximum power point tracking(MPPT)method and make the photo voltaic(PV)system work more quickly and accurately at the maximum power output point,an adaptive control method based on fuzzy control and neural network control was proposed.This method made full use of the advantages of fuzzy neural network to deal with nonlinear problems.The fuzzy control was used to change the step size,and the self-learning ability of the neural network was used to achieve the balance quickly.The PV MPPT achieved a better balance between tracking speed and stability.Simulation and experimental results showed that the MPPT method based on fuzzy neural network adaptive control had strong robustness and adaptive ability.