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Speed estimation of an induction motor drive using an optimized extended Kalman filter

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4 Author(s)
K. L. Shi ; Dept. of Electr. Eng., Hong Kong Polytech. Univ., Kowloon, China ; T. F. Chan ; Y. K. Wong ; S. L. Ho

This paper presents a novel method to achieve good performance of an extended Kalman filter (EKF) for speed estimation of an induction motor drive. A real-coded genetic algorithm (GA) is used to optimize the noise covariance and weight matrices of the EKF, thereby ensuring filter stability and accuracy in speed estimation. Simulation studies on a constant V/Hz controller and a field-oriented controller (FOC) under various operating conditions demonstrate the efficacy of the proposed method. The experimental system consists of a prototype digital-signal-processor-based FOC induction motor drive with hardware facilities for acquiring the speed, voltage, and current signals to a PC. Experiments comprising offline GA training and verification phases are presented to validate the performance of the optimized EKF

Published in:

IEEE Transactions on Industrial Electronics  (Volume:49 ,  Issue: 1 )