Speed and rotor flux estimation of induction motors based on extended kalman filter

Two problems must be solved in the speed sensorless vector control of induction motor drive: the speed estimation and rotor flux observation. Because of the multiplication terms of state variables, the induction motor model is the non-linear state equations. To estimate the state variables of motor model and gain the rotor flux and speed signals, the paper proposes a method to estimate them using extended kalman filter. Experiment is based on the DSP design system for digital motor control. Software programs carry out extended kalman filter algorithm to estimate the rotor speed and fluxes. The satisfied experimental results prove that extended kalman filter algorithm can real time estimate rotor speed and flux very accurately, and based on which the speed sensorless drive system has good static and dynamic performance.

[1]  Lu Zhen,et al.  Sensorless field orientation control of induction machines based on a mutual MRAS scheme , 1998, IEEE Trans. Ind. Electron..

[2]  Lennart Ljung System Identification and Adaptive Control , 1982 .

[3]  J. Clare,et al.  MRAS observer for doubly fed induction Machines , 2004, IEEE Transactions on Energy Conversion.

[4]  K. Matsuse,et al.  Speed sensorless field oriented control of induction motor with rotor resistance adaptation , 1993, Conference Record of the 1993 IEEE Industry Applications Conference Twenty-Eighth IAS Annual Meeting.

[5]  C. Schauder,et al.  Adaptive speed identification for vector control of induction motors without rotational transducers , 1989, Conference Record of the IEEE Industry Applications Society Annual Meeting,.

[6]  R.D. Lorenz,et al.  Observer-based direct field orientation: analysis and comparison of alternative methods , 1993, Conference Record of the 1993 IEEE Industry Applications Conference Twenty-Eighth IAS Annual Meeting.

[7]  Liu Shu-xi Rotor Resistance Identification in Vector Control System of Asynchronous Motor Based on EKF , 2009 .