电机与控制应用2017，Vol.44Issue(5)：52-55,76,5.
基于优化BP神经网络的开关磁阻电机定子电阻辨识方法
Stator Resistance Identification Method of Switched Reluctance Motor Based on Optimized BP Neural Network
摘要
Abstract
When switched reluctance motor was in the status of slow running under direct torque control,calculation of flux was greatly influenced by resistance.In order to solve the issue above.The study observed and analyzed carefully about the relation between resistance and phase current,through comparing the output current between resistance variable motor model and actual motor model,proposed a solution of resistance estimation based on optimized BP neural networks.Optimized BP neural networks had sufficient mathematical theory,with simple structure and clear algorithm.The algorithm based on BP neural networks could recognize variable stator resistance.Put this algorithm into action in the Simulink control system,then comparing the test results between with resistance estimation and without resistance estimation.Experimental results showed that this resistance estimation method could improve system performance when the switched reluctance motor was in the status of slow running.关键词
直接转矩控制/开关磁阻电机/优化BP神经网络/定子电阻辨识Key words
direct torque control (DTC)/switched reluctance motor (SRM)/optimized BP neural networks/stator resistance estimation分类
信息技术与安全科学引用本文复制引用
许爱德,赵中林,王雪松..基于优化BP神经网络的开关磁阻电机定子电阻辨识方法[J].电机与控制应用,2017,44(5):52-55,76,5.基金项目
国家自然科学青年基金(51407021) （51407021）
中央高校基本科研业务费(3132015214) （3132015214）
