摘要
针对光伏并网逆变器系统动态响应缓慢、传统PID控制并网电流跟踪效果差的问题,提出BP神经网络结合PID的控制方法。取单相LCL型并网逆变器为研究对象,分析单相LCL型并网逆变器电路结构以及BP神经网络模型,通过采用具有动量更新的反向传播BP学习算法加快误差性能函数收敛,实时快速地输出合适的PID参数,以提高系统响应速度。最后构建Matlab模型进行仿真,仿真结果表明,相较于传统PID控制器,BP结合PID的控制策略能更好地完成对并网电流跟踪,且速度更快、稳态误差更小,验证了该方法的有效性。
In view of the flaws of slow dynamic response of photovoltaic grid-connected inverter system and poor grid-connected current tracking effect of traditional PID control,a control method of BP neural network combined with PID has thus been proposed.With single-phase LCL grid-connected inverter being the research object,an analysis has been made of the circuit structure and BP neural network model of single-phase LCL grid-connected inverter.By adopting the back-propagation BP learning algorithm with a momentum update,the convergence of error performance function is accelerated,followed by a real-time quick output of appropriate PID parameters,thus improving the response speed of the system.Finally,the Matlab model is constructed for simulation.The simulation results show that compared with the traditional PID controller,the control strategy of BP combined with PID is characterized with a better performance in completing the grid-connected current tracking,with a faster speed as well as a smaller steady-state error,thus verifying the effectiveness of the method.
作者
屈浩轩
陈刚
董和夫
李江坪
乔超杰
虞佳兴
QU Haoxuan;CHEN Gang;DONG Hefu;LI Jiangping;QIAO Chaojie;YU Jiaxing(College of Electrical and Information Engineering,Hunan University of Technology,Zhuzhou Hunan 412007,China)
出处
《湖南工业大学学报》
2022年第2期34-40,共7页
Journal of Hunan University of Technology
基金
国家自然科学基金资助面上项目(62173136)。
关键词
单相LCL型光伏并网逆变器
并网电流
PID控制
BP神经网络
single-phase LCL type grid-connected photovoltaic inverter
grid-connected current
PID control
BP neural network
