摘要
开关磁阻电机(Switched Reluctance Motor,SRM)的双凸极结构导致其运行时产生很强的转矩脉动,采用传统型转矩分配函数(Torque Sharing Function,TSF)时,电流峰值过高,铜耗过大,导致电机效率低,转矩脉动抑制效果不理想。针对这一问题,本文提出了新型分段式TSF,并以转矩脉动系数和电流变化率作为优化目标,利用遗传算法的全局寻优能力,对所提出的新型TSF中的变量参数进行优化,得到最优的TSF曲线及表达式,从而降低转矩脉动。仿真结果表明,本文所提出的新型TSF能够有效降低转矩脉动,同时降低电流峰值,减小铜耗,提高效率。
The double salient pole structure of a switched reluctance motor(SRM)results in a strong torque ripple during operation.When the traditional torque sharing function(TSF)is used,the current peak is too high,copper consumption is too large,efficiency is low,which lead to low motor efficiency and unsatisfactory torque ripple suppression.Aiming at this problem,a novel segmented TSF is proposed in this paper,the torque ripple and current change rate are taken as the optimization goal.The global optimization ability of the genetic algorithm is used to optimize the variable parameters in the proposed novel TSF to obtain the optimal TSF curve and expressions to reduce torque ripple.The simulation results show that the proposed novel TSF can effectively reduce torque ripple,reduce current peak,reduce copper consumption and improve efficiency.
作者
张嘉贺
顾国彪
ZHANG Jiahe;GU Guobiao(Institute of Electrical Engineering Chinese Academy of Sciences,Beijing 100190,China;University of Chinese Academy of Sciences,Beijing 100049,China)
出处
《大电机技术》
2020年第1期43-48,共6页
Large Electric Machine and Hydraulic Turbine
关键词
开关磁阻电机
转矩脉动
遗传算法
转矩分配函数
switched reluctance motor
torque ripple
genetic algorithm
torque sharing function
