To maximize efficiency, the speed of an induction motor (IM) is controlled to match the load. Often an extended Kalman filter (EKF) estimates the speed of the IM, eliminating the speed sensor. The EKF requires a mathematical model of the IM and system and noise covariances, typically determined by optimization using trial-and-error or a genetic algorithm (GA). My research objective was to investigate a relatively new algorithm, the enhanced fireworks algorithm (EFWA) and its ability to determine covariances compared to current methods. I used a Simulink model of a system comprised of an IM controlled by a variable frequency drive (VFD) to experiment with the EKF using trial and error, genetic algorithm and EFWA optimization methods. My results indicated that in this application, using selected parameters, the EFWA provides a good solution in fewer iterations than the GA, which may be required for online adaptive tuning of the EKF.
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Thesis advisor: Lee, Daniel C.
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