三相脉宽调制整流器的约束预测控制

本文将基于并行神经网络优化的约束模型预测控制(MPC)应用于脉宽调制(PWM)整流器中,提高了电网的质量.在三相静止坐标系下,建立了三相PWM整流器的解耦数学模型,采用约束模型预测控制策略,突破了有限集和无约束条件下预测控制的局限性.为了提高单步优化的速度,采用神经网络优化算法求解模型预测控制的在线优化.在保证系统单位功率因数的前提下,当系统负载突然变化时,具有快速动态响应稳定输出直流电压的性能.采用FPGA控制器实现并行计算,减少了预测控制算法的计算时间.最后,通过仿真和实验结果得到,采用本文的控制策略,总谐波失真(THD)降低了2.5%,达到稳态的时间大约是PI控制算法的五分之一,为12 ms,验证了该方法的可行性和有效性.

Constrained model predictive control for a three-phase pulse width modulation rectifier

In this paper, constrained model predictive control (MPC) based on parallel neural network optimization is proposed to apply to pulse width modulation (PWM) rectifier and improve power quality. A decoupled model of three-phase rectifier under three-phase stationary coordinates system is built. Then, the constrained MPC method is proposed. This method breaks the limits of predictive control with finite set and without constraints. Neural network optimization is used to solve online optimization of MPC and accelerate single step optimization. An approach is proposed to guarantee unity power factor, which also provides a regulated output dc-voltage with fast dynamics response against sudden changes in the load. A FPGA controller is employed to implement parallel calculation so as to reduce the computation time on predictive control algorithm. Simulation and experimental results demonstrate effectiveness and feasibility of the proposed method.