摘要
针对并网逆变器时变与非线性的特点,将自适应学习率并带有动量因子的BP神经网络应用于准PR控制器中,提高了系统的自适应能力,使并网电流的畸变程度降低。首先设计了谐波补偿环节,对含量较高的奇次谐波频率构成新的传递函数,再提取误差电流中含量较高3、5、7次谐波,应用改进BP神经网络来自适应调节补偿增益,提高了收敛速度与补偿精度。MATLAB/Simulink仿真研究表明,该方法降低了电流总谐波畸变率,使逆变系统具有了快速动态响应的能力,提升了系统的稳定程度。
According to the time-varying and nonlinear characteristics of grid-connected inverter,BP neural network with adaptive learning rate and momentum factor is applied to quasi-PR controller,which improves the adaptive ability of the system and reduces the distortion of the grid current.Firstly,the harmonic compensation link is designed,which constitutes a new transfer function for the higher-order odd harmonics' frequency,and then detect the 3-order,5-order and 7-order harmonics with higher content in the error current,and applies the improved BP neural network to adaptively adjust the compensation.The method accelerates the convergence rate and improves compensation accuracy.Matlab/simulink simulation results show that compared with quasi-PR control,the neural network quasi-PR control method decreases the total harmonic distortion of tracking current,increases the dynamic response performance,and improves the stability of the system.
作者
鲁改凤
杜帅
欧钰雷
张帅
Lu Gaifeng;Du Shuai;Ou yulei;Zhang Shuai(School of Electric Power,North China University of Water Resources and Electric Power,Zhengzhou 450045,China)
出处
《电测与仪表》
北大核心
2019年第19期59-63,共5页
Electrical Measurement & Instrumentation
基金
华北水利水电大学第九届研究生创新课题(YK2017-07)
关键词
并网逆变器
谐波补偿
准PR控制
BP神经网络
grid-connected inverter
harmonic compensation
quansi-PR control
BP neural network
