An online identification technology for rotor resistance of asynchronous motors
In response to the problem of significant deviation in the identification results of asyn-chronous motor rotor resistance,this paper proposes an online identification technology for asynchronous motor rotor resistance based on fusion neural networks.This technical solution uses Model Reference Adaptive System(MRAS)as the basic framework,voltage model flux observation system as the reference unit,and current model flux observation system as the control unit.A rotor resistance identification meth-od based on voltage and current models is designed.Furthermore,the reference unit system model is trained and optimized through convolutional neural network to realize the accurate identification of asyn-chronous motor rotor resistance online.The experimental test results show that this technical solution is highly feasible,with a response speed improvement of about 25％compared to similar methods for rotor resistance identification.The recognition accuracy has also reached a high level,and it has strong anti-interference stability.This provides a new idea for the design of vector control strategies for asynchronous motors.
asynchronous motorrotor resistanceneural networkMRASvector control
万方数据
维普
