TELKOMNIKA Telecommunication Computing Electronics and Control
Comparison of the speedy estimate methods of the induction motors

TELKOMNIKA Telecommunication Computing Electronics and Control
Comparison of the speedy estimate methods of the induction motors

Extended Kalman filter
Flux oriented control
GA algorithm
Induction motor drive
Sensorless
Speed estimation

This paper deals with a novel method to achieve the effective performance
of the extended Kalman filter (EKF) for the speedy estimate of an induction
motor. The real coding genetic algorithm (GA) is used to optimize the
components of the covariance matrix in the EKF, thus ensuring the stability
and accuracy of the filter in the speed estimation. The advantage of the
proposed method is less dependent on the parameters of the induction motor.
The content includes the vector control model for induction motor, the speed
estimation by modeling the reference frame-model reference adaptive
system (RF-MRAS), the current based-model reference adaptive system
(CB-MRAS), and the speed estimation with the EKF optimized by genetic
algorithm. Simulative studies on the field-oriented controller (FOC) with
different operating conditions are performed in Matlab Simulink when the
rotor resistance changes in the current speed estimation methods. The
simulation results demonstrate the efficiency of the proposed GA-EKF filter
compared with other speed estimation methods of induction motors.

Thinh Cong Tran, Pavel Brandstetter, Hau Huu Vo, Chau Si Thien Dong, Martin Kuchar

http://telkomnika.uad.ac.id

Nov 25, 2022

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