基于递归神经网络定子磁链观测器的凸极同步电动机直接转矩控制系统

针对传统的凸极同步电动机直接转矩控制系统定子磁链观测器存在积分器漂移等问题,提出了一种基于递归神经网络定子磁链观测器的凸极同步电动机直接转矩控制系统的设计方案。该方案将三相电压与三相电流经3S/2S变换后得到的两相电压与电流送到已经训练好的基于递归神经网络的定子磁链观测器中,观测器的输出是定子磁链的α、β分量,即Ψsα、Ψsβ;Ψsα、Ψsβ经矢量分析器处理后得到定子磁链的幅值以及定子磁链的空间位置角,从而可准确得到定子磁链所在的扇区。仿真结果表明,与基于传统的U-I模型的凸级同步电动机直接转矩控制系统相比,该系统具有优良的动、静态性能。

Direct Torque Control System of Salient Pole Synchronous Motor with Stator Flux Observer Based on Recurrent Neural Network

In view of the problem of drifting of integrator existed in stator flux observer of traditional direct torque control system of salient pole synchronous motor,the paper proposed a design scheme of direct torque control system of salient pole synchronous motor with stator flux observer based on recurrent neural network.In the scheme,two-phase voltage and current transformed from three-phase voltage and current by 3S/2S are sent to trained stator flux observer based on recurrent neural network whose outputs are α,β components of stator flux,that is Ψ_(sα),Ψ_(sβ),then amplitude and space angle of stator flux are obtained after Ψ_(sα),Ψ_(sβ) are processed by vector analyzer,so as to obtain sector location of stator flux accurately.The simulation results showed that the system has good dynamic and static performance comparing with direct torque control system of salient pole synchronous motor based on traditional U-I model.