基于卷积神经网络的光伏发电系统最大功率点跟踪方法

Maximum Power Point Tracking Method of Photovoltaic Power Generation System Based on Convolutional Neural Network

常规的最大功率点跟踪方法以光伏发电特征分析为主,受到复杂光照条件的影响,最大功率点跟踪结果存在一定的偏差。因此,文章设计了基于卷积神经网络的光伏发电系统最大功率点跟踪方法。通过提取光伏发电系统最大功率点输出特征,分析光伏列阵的功率-电压变化,构建最大功率点电压扰动方程,确定光伏发电系统最大功率点位置。基于卷积神经网络构建最大功率点跟踪控制电路,并通过调整电路占空比改变输出电压,实时跟踪最大功率点。采用对比实验,验证了该方法的跟踪准确性更高,能够应用于实际生活。

The conventional maximum power point tracking method is mainly based on the analysis of photovoltaic power generation characteristics.Due to the influence of complex lighting conditions,the maximum power point tracking results have certain deviations.Therefore,this paper designs the maximum power point tracking method of photovoltaic power generation system based on convolutional neural network.By extracting the output characteristics of the maximum power point of photovoltaic power generation system,the power-voltage change of photovoltaic array is analyzed,and the voltage disturbance equation of the maximum power point is constructed to determine the position of the maximum power point of photovoltaic power generation system.The maximum power point tracking control circuit is constructed based on convolutional neural network,and the output voltage is changed by adjusting the duty cycle of the circuit to track the maximum power point in real time.The comparative experiments show that the tracking accuracy of this method is higher and it can be applied to real life.