Using NSGA II multiobjective genetic algorithm for EKF-based estimation of speed and electrical torque in AC induction machines
High-performance AC drives require accurate speed and torque estimations to provide a proper system operation. The selection and quality of extended Kaiman fitter (EKF) covariance elements have a considerable bearing on the effectiveness of motor drive. Many EKF-based optimization techniques involve...
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| Main Authors: | , , , , |
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| Format: | Conference or Workshop Item |
| Published: |
2014
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| Subjects: | |
| Online Access: | http://eprints.utm.my/63188/ http://eprints.utm.my/63188/ |
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| Summary: | High-performance AC drives require accurate speed and torque estimations to provide a proper system operation. The selection and quality of extended Kaiman fitter (EKF) covariance elements have a considerable bearing on the effectiveness of motor drive. Many EKF-based optimization techniques involve only a single objective for the optimal estimation of speed without giving concern to the torque. This paper presents a new methodology for the selection of EKF filters that uses non-dominated sorting genetic algorithm-II (NSGA-II) developed for filter element selection in order to investigate the concurrent optimization of speed and torque. The proposed optimizing technique for EKF-based estimation scheme is used in the combination with the sensorless direct torque control of induction motor. The multi-optimal based-EKF is tested with three regions of Pareto front curve. |
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