基于水循环算法的开关磁阻电机性能优化

Performance Optimization of Switched Reluctance Motor Based on Water-cycle Algorithm

针对由于开关磁阻电机的双凸极结构和磁路饱和非线性特性引起的转矩脉动较大及性能较差的问题,提出了一种基于转速控制和转矩控制的联合控制策略对其性能进行优化.首先,设计了一种基于水循环算法(WCA)优化的小波神经网络(WNN)控制器,根据该控制器设计了速度控制器,用于对未知参数变化和外部负载扰动带来的系统控制误差进行在线调节.此外,在内环,采用改进的直接瞬时转矩控制(DITC)直接对转矩进行调节,尽可能地减少转矩脉动.运用Simulink搭建仿真模型并与其它控制算法进行对比.最后,在Maxwell中基于实际电机参数搭建电机模型,利用Simulink及Maxwell的联合仿真进一步验证算法的有效性.仿真结果表明,所提出的方法能够有效地应对外部负载扰动等情况,具有很强的鲁棒性.

To address the problem in which a double-salient structure and nonlinear magnetic circuit saturation cause torque ripple and poor performance in the switched reluctance motor( SRM),we present a combined strategy of speed control and torque control to optimize its performance. First,we design a controller based on the water-cycle algorithm( WCA) to optimize the weights of the wavelet neural network( WNN). Based on the proposed controller,we then design a speed controller to make online adjustments of errors caused by unknown variation and external load disturbance. Next,we use an improved direct instantaneous torque control( DITC) to minimize the torque ripple in the inner loop. Finally,we built a model in Simulink and compared its performance with those of other methods,and then built a motor model in Maxwell based on the actual motor parameters. Based on a joint simuation of Simulink and Maxwell,we further verify the effectiveness of this method. The results show that the proposed method can effectively adapt to the external load disturbance and has strong robustness.