Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/892
Title: Speed estimation of an induction motor drive using an optimized extended Kalman filter
Authors: Shi, Keli
Chan, T. F.
Wong, Yiu-kwong
Ho, Siu-lau
Subjects: Genetic algorithm
Induction motor
Kalman filter
Issue Date: Feb-2002
Publisher: IEEE
Source: IEEE transactions on industrial electronics, Feb. 2002, v. 49, no. 1, p. 124-133.
Abstract: 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.
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Type: Journal/Magazine Article
URI: http://hdl.handle.net/10397/892
DOI: 10.1109/41.982256
ISSN: 0278-0046
Appears in Collections:EE Journal/Magazine Articles

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