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A Very-Low-Speed Sensorless Control Induction Motor Drive with Online Rotor Resistance Tuning by Using MRAS Scheme Cover

A Very-Low-Speed Sensorless Control Induction Motor Drive with Online Rotor Resistance Tuning by Using MRAS Scheme

Open Access
|Nov 2019

Abstract

A sensorless indirect stator-flux-oriented control (ISFOC) induction motor drive at very low frequencies is presented herein. The model reference adaptive system (MRAS) scheme is used to estimate the speed and the rotor resistance simultaneously. However, the error between the reference and the adjustable models, which are developed in the stationary stator reference frame, is used to drive a suitable adaptation mechanism that generates the estimates of speed and the rotor resistance from the stator voltage and the machine current measurements. The stator flux components in the stationary reference frame are estimated through a pure integration of the back electro-motive force (EMF) of the machine. When the machine is operated at low speed, the pure integration of the back EMF introduces an error in flux estimation which affects the performance torque and speed control. To overcome this problem, pure integration is replaced with a programmable cascaded low-pass filter (PCLPF). The stability analysis method of the MRAS estimator is verified in order to show the robustness of the rotor resistance variations. Experimental results are presented to prove the effectiveness and validity of the proposed scheme of sensorless ISFOC induction motor drive.

DOI: https://doi.org/10.2478/pead-2018-0021 | Journal eISSN: 2543-4292 | Journal ISSN: 2451-0262
Language: English
Page range: 125 - 140
Submitted on: Sep 2, 2018
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Accepted on: Oct 8, 2018
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Published on: Nov 26, 2019
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year

© 2019 Youssef Agrebi Zorgani, Mabrouk Jouili, Yassine Koubaa, Mohamed Boussak, published by Wroclaw University of Science and Technology
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.