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10.1109_ojies.2021.3072232.pdf (7.24 MB)

Sensorless Field-Oriented Control for Open-End Winding Five-Phase Induction Motor With Parameters Estimation

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journal contribution
submitted on 2023-08-29, 08:33 and posted on 2023-08-29, 12:25 authored by Saad Khadar, Haitham Abu-Rub, Abdellah Kouzou

This paper proposes a sensorless field-oriented control (FOC) of an open-end stator winding five-phase induction motor (OESW-FPIM). The FOC technique used is associated with dual Space Vector Modulation (SVM) to provide a constant switching frequency and lower harmonics distortion. Furthermore, a simple hybrid observer is proposed which combines a model reference adaptive system (MRAS) and a sliding mode (SM) observer. The examined observer is designed for the estimation of the rotor flux and rotational speed as well as for the estimation of the load torque disturbances. Lyapunov theorem is used in this paper to prove the observer's stability. The work presented in this paper aims to enhance the researched motor's sensorless control and its robustness against external load disturbances and parameters variation. In the proposed MRAS-SM observer, the reference model is replaced by a SM model which uses a sigmoid function as a switching function to overcome the chattering problem. This combination is intended to make use of the advantages of both strategies. At the same time, to preserve the high-level performance of the sensorless FOC technique and to reduce system uncertainties, an estimation algorithm is developed to identify the rotor resistance and the stator resistance simultaneously during motor operation. The parameter estimation algorithm is combined with the proposed control to improve the speed estimation and control accuracy, particularly at low-speed operation. Finally, the effectiveness of the proposed control is validated in real-time by utilizing a hardware-in-the-loop (HIL) platform.

Other Information

Published in: IEEE Open Journal of the Industrial Electronics Society - Volume: 2
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  • English



Publication Year

  • 2021

License statement

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

Institution affiliated with

  • Texas A&M University at Qatar