基于神经网络的光伏阵列多峰MPPT的研究

为了减少神经网络训练数据的数量,根据局部阴影条件下光伏阵列的输出特性,提出基本阴影遮挡类型概念,使得神经网络仅需要训练少量数据,就可以准确地预测最大功率点电压。首先,通过实际光伏阵列数据测试仅训练基本阴影遮挡类型的BP神经网络对最大功率点电压的跟踪效果。然后,搭建光伏发电MPPT仿真系统,对比扰动法、固定电压法和BP神经网络结合扰动法在阴影类型、光照强度和温度三方面变化时对MPP的跟踪效果。最后,通过分析表明,经过基本阴影遮挡类型训练的BP神经网络结合扰动法能够有效地跟踪最大功率点,即基本阴影遮挡类型能够减少神经网络跟踪多峰MPP的训练数据获取量。

Research on multi-peak MPPT of PV array based on neural network

For reducing the number of neural network training data, according to the output of the photovoltaic array under local shadow conditions, the concept is proposed about basic shadow occlusion type, so that the neural network only train small amount of data and the voltage of MPP can be accurately predicted. Firstly, the actual PV array data used to test the effect of BP neural network training on the tracking of the voltage of MPP only with the basic shadow shading type. Then, built the MPPT simulation system of photovoltaic power generation system and compared the perturbation method, fixed voltage method and BP neural network combined with the perturbation method to track the MPP. Finally, the analysis shows that the BP neural network combined with the perturbation method trained by the basic shadow occlusion type can effectively track the MPP, that is, the basic shadow occlusion type can reduce the acquisition of training data when the neural network tracks multi-peak MPPT.