基于神经网络的感应电机软起动控制器设计

Design induction machines’ controller in soft-start based on neural networks

以软起动器驱动的感应电机为研究对象,利用电路计算和傅里叶分析获得起动过程定子开路电压、电流的有效值和相应谐波的线幅,将已有的斜坡软起动数据作为样本训练电压电流—反电势转速网络,将软起动模态数据作为网络输入,得到辨识的反电势和转子转速的有效值,并提出一种基于转速信息的新控制器,实现对电机的软起动控制。仿真结果表明,控制器能够不仅能够保证系统节能,而且能有效抑制起动瞬间的转矩振荡。

Oriented to soft starter driven induction machines, the values of open-circuit voltage, stator current and the harmonics’ line spectrum were accquired by means of circuit calculations and Fourier analysis. The neural network of voltage current—back electromotive force(BEMF) speed were trained by simulation data of voltage slope soft-start, and the identified values of BEMF and speed were obtained by exciting the network with soft-start mode’s data. A new control pricinples containing speed’s information were provided to realize motor’s soft start. Simulation results indicate it can not only save more energy, but also reduce suppress output torque’s viberations at the beginning of start.