<abstract xmlns="http://www.w3.org/1999/xhtml"><p>This paper aims to introduce a novel extended Kalman filter (EKF) based estimator including observability analysis to the literature associated with the high performance speed-sensorless control of induction motors (IMs). The proposed estimator simultaneously performs the estimations of stator stationary axis components of stator currents and rotor fluxes, rotor mechanical speed, load torque including the viscous friction term, and reciprocal of total inertia by using measured stator phase currents and voltages. The inertia estimation is done since it varies with the load coupled to the shaft and affects the performance of speed estimation especially when the rotor speed changes. In this context, the estimations of all mechanical state and parameters besides flux estimation required for high performance control methods are performed together. The performance of the proposed estimator is tested by simulation and real-time experiments under challenging variations in load torque and velocity references; and in both transient and steady states, the quite satisfactory estimation performance is achieved.</p></abstract>

This paper aims to introduce a novel extended Kalman filter (EKF) based estimator including observability analysis to the literature associated with the high performance speed-sensorless control of induction motors (IMs). The proposed estimator simultaneously performs the estimations of stator stationary axis components of stator currents and rotor fluxes, rotor mechanical speed, load torque including the viscous friction term, and reciprocal of total inertia by using measured stator phase currents and voltages. The inertia estimation is done since it varies with the load coupled to the shaft and affects the performance of speed estimation especially when the rotor speed changes. In this context, the estimations of all mechanical state and parameters besides flux estimation required for high performance control methods are performed together. The performance of the proposed estimator is tested by simulation and real-time experiments under challenging variations in load torque and velocity references; and in both transient and steady states, the quite satisfactory estimation performance is achieved.

Extended Kalman Filter Based Speed-Sensorless Load Torque and Inertia Estimations with Observability Analysis for Induction Motors

Power Electronics and Drives

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

This paper aims to introduce a novel extended Kalman filter (EKF) based estimator including observability analysis to the literature associated with the high...

Extended Kalman Filter Based Speed-Sensorless Load Torque and Inertia Estimations with Observability Analysis for Induction Motors

Article Category: Special Section on Sensorless Drives

Published Online: Nov 17, 2018

Page range: 115 - 127

Received: Aug 21, 2017

Accepted: Feb 16, 2018

DOI: https://doi.org/10.2478/pead-2018-0002

Keywords
Extended Kalman filter, Induction motor, Load torque and inertia estimation, Speed-sensorless control, Observability analysis

© 2018 Emrah Zerdali, Murat Barut, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Extended Kalman Filter Based Speed-Sensorless Load Torque and Inertia Estimations with Observability Analysis for Induction Motors

Article Category: Special Section on Sensorless Drives

Published Online: Nov 17, 2018

Page range: 115 - 127

Received: Aug 21, 2017

Accepted: Feb 16, 2018

DOI: https://doi.org/10.2478/pead-2018-0002

KeywordsExtended Kalman filter, Induction motor, Load torque and inertia estimation, Speed-sensorless control, Observability analysis

© 2018 Emrah Zerdali, Murat Barut, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Keywords
Extended Kalman filter, Induction motor, Load torque and inertia estimation, Speed-sensorless control, Observability analysis