基于智能优化与磁链预测的双馈电机直接转矩控制

Direct Torque Control of Doubly Fed Induction Motor Based on Flux Linkage Prediction and Intelligent Optimization

传统的直接转矩控制(direct torque control,DTC)应用于双馈电机时,双逆变装置使DTC原生的转矩脉动加剧。为此,提出智能优化与磁链预测控制相结合的方法,利用预测控制减小转矩脉动,遗传算法优化系统控制参数,在线克服电机与控制系统的非线性。采用Matlab的M-file进行仿真,结果表明,该方法有效降低了双馈电机运行中的噪声和振动,亦提高了系统的鲁棒性。

When traditional applied on doubly fed induction direct torque control （DTC） is motor, the dual-inverter makes the torque pulsation supervened with DTC intensified. For this reason, a method combining intelligent optimization with flux linkage prediction, which reduces torque pulsation by predictive control, is proposed and the genetic algorithm （GA） is adopted to optimize control parameters of the system to overcome nonlinearity of both induction motor and control system in on-line way. The proposed control system is simulated by M-file in Matlab, and simulation results show that the proposed control method can effectively mitigate the noise and oscillation during the operation of doubly fed induction motor, and the robustness of the control system is improved.