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作 者:周习祥[1] ZHO U Xixiang(Yiyang Vocational Technical College,Yiyang 413049,China)
出 处:《四川理工学院学报(自然科学版)》2018年第5期21-28,共8页Journal of Sichuan University of Science & Engineering(Natural Science Edition)
基 金:湖南省自然科学基金(2017JJ5048)
摘 要:建立了d-q坐标系下的三相电压型PWM整流器(VSR)数学模型与前馈解耦状态方程,分析了基于d-q变换与空间矢量脉宽调制(SVPWM)的三相VSR双闭环控制系统,针对传统的PI控制器在负载特性、VSR工作模式发生变化时,容易引起超调,使控制系统不能达到良好的控制效果这一问题,对传统三相VSR控制系统进行了改进,利用BP神经网络的自学习功能,设计了BPNN自适应PID控制器,实现PID控制器参数最优化;在Matlab/Simulink环境下搭建了基于BPNN自适应PID的三相VSR控制系统仿真电路,得到了三相VSR工作模式变化和负载突变时的仿真波形,仿真结果验证了该控制系统设计的准确性和有效性。A three-phase voltage type PWM rectifier d-q coordinates (VSR) mathematical model and feedforward deeoupling state equation are established and the three-phase VSR double closed loop control system based on d-q transform and space vector pulse width modulation (SVPWM) is analyzed. The traditional PI controller is prone to cause overshoot when load characteristics and VSR working mode change, so that the control system can not achieve good control effect. The traditional three-phase VSR control system is improved by using the self-learning function of the BP neural network design BPNN adaptive PID controller to realize PID controller parameter optimization. In the Matlab/Simulink environment, a three- phase VSR control system simulation circuit based on BPNN adaptive PID is built, and the simulation waveforms of three phase VSR working mode and sudden change of load are obtained. The simulation results show the accuracy and effectiveness of the control system design
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