Miloud, I.; Cauet, S.; Etien, E.; Salameh, J.P.; Ungerer, A. Real-Time Speed Estimation for an Induction Motor: An Automated Tuning of an Extended Kalman Filter Using Voltage–Current Sensors. Sensors2024, 24, 1744.
Miloud, I.; Cauet, S.; Etien, E.; Salameh, J.P.; Ungerer, A. Real-Time Speed Estimation for an Induction Motor: An Automated Tuning of an Extended Kalman Filter Using Voltage–Current Sensors. Sensors 2024, 24, 1744.
Miloud, I.; Cauet, S.; Etien, E.; Salameh, J.P.; Ungerer, A. Real-Time Speed Estimation for an Induction Motor: An Automated Tuning of an Extended Kalman Filter Using Voltage–Current Sensors. Sensors2024, 24, 1744.
Miloud, I.; Cauet, S.; Etien, E.; Salameh, J.P.; Ungerer, A. Real-Time Speed Estimation for an Induction Motor: An Automated Tuning of an Extended Kalman Filter Using Voltage–Current Sensors. Sensors 2024, 24, 1744.
Abstract
This paper aims at achieving optimal rotor speed estimation for an induction motor using the Extended Kalman filter (EKF). Speed estimation is essential for fault diagnosis in Motor Current Signature Analysis (MCSA). The estimation accuracy is obtained by exploring the noise covariance matrices estimation of the EKF algorithm. The noise covariance matrices are determined using a modified subspace model identification approach. In order to reach this goal, this method compares an estimated model of a deterministic system, derived from available input-output datasets, with the discrete-time state-space representation used in the Kalman filter equations. This comparison leads to the determination of model uncertainties, which are subsequently represented as noise covariance matrices. Based on the 5th order nonlinear model of the induction motor, rotor speed is estimated with the optimized EKF algorithm and the used algorithm is tested experimentally.
Keywords
condition monitoring; Kalman filter; noise covariance matrix; subspace model identification; induction motor
Subject
Engineering, Control and Systems Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
