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.
Original language | English |
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Pages (from-to) | 124-133 |
Number of pages | 10 |
Journal | IEEE Transactions on Industrial Electronics |
Volume | 49 |
Issue number | 1 |
DOIs | |
Publication status | Published - 1 Feb 2002 |
Keywords
- Genetic algorithm
- Induction motor
- Kalman filter
ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Instrumentation