Abstract
<span lang="EN-US">This paper presents the comparison between optimized unscented Kalman filter (UKF) and optimized extended Kalman filter (EKF) for sensorless direct field orientation control induction motor (DFOCIM) drive. The high performance of UKF and EKF depends on the accurate selection of state and noise covariance matrices. For this goal, multi objective function genetic algorithm is used to find the optimal values of state and noise covariance matrices. The main objectives of genetic algorithm to be minimized are the mean square errors (MSE) between actual and estimation of speed, current, and flux. Simulation results show the optimal state and noise covariance matrices can improve the estimation of speed, current, torque, and flux in sensorless DFOCIM drive. Furthermore, optimized UKF present higher performance of state estimation than optimized EKF under different motor operating conditions.</span>
Publisher
Institute of Advanced Engineering and Science
Subject
Electrical and Electronic Engineering,General Computer Science
Cited by
2 articles.
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1. Kalman Filter as Part of a Relay-Vector System Control of Asynchronous Electric Drive;2023 IEEE 5th International Conference on Modern Electrical and Energy System (MEES);2023-09-27
2. Closed Loop Control of Induction Motor Using Hall Effect Speed Sensors;2023 3rd International Conference on Pervasive Computing and Social Networking (ICPCSN);2023-06