Using NSGA II multiobjective genetic algorithm for EKF-based estimation of speed and electrical torque in AC induction machines

• Main Authors: Mohd. Alsofyani, Ibrahim, Nik Idris, Nik Rumzi, Jannati, Mohammad, Anbaran, Sajad Abdollahzadeh, Alamri, Yahya Ahmed
• Format: Conference or Workshop Item
• Published: 2014
• Subjects: TK Electrical engineering. Electronics Nuclear engineering
• Online Access: http://eprints.utm.my/id/eprint/63188/; http://dx.doi.org/10.1109/PEOCO.2014.6814461
• Institution: Universiti Teknologi Malaysia

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.