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A Comprehensive Examination of Vector-Controlled Induction Motor Drive Techniques

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  • Ahmed G. Mahmoud A. Aziz

    (Electrical Engineering Department, Faculty of Engineering, Minia University, Minia 61111, Egypt
    Electrical and Computer Department, Higher Institute of Engineering and Technology New El Minia, Minia 61111, Egypt)

  • Almoataz Y. Abdelaziz

    (Faculty of Engineering & Technology, Future University in Egypt, Cairo 11835, Egypt)

  • Ziad M. Ali

    (College of Engineering at Wadi Addawasir, Prince Sattam bin Abdulaziz University, Wadi Addawasir 11991, Saudi Arabia
    Electrical Engineering Department, Faculty of Engineering, Aswan University, Aswan 81542, Egypt)

  • Ahmed A. Zaki Diab

    (Electrical Engineering Department, Faculty of Engineering, Minia University, Minia 61111, Egypt)

Abstract

This paper introduces a comprehensive examination of vector-controlled- (VC-) based techniques intended for induction motor (IM) drives. In addition, the evaluation and critique of modern control techniques that improve the performance of IM drives are discussed by considering a systematic literature survey. Detailed research on variable-speed drive control, for instance, VC and scalar control (SCC), was conducted. The SCC-based systems’ speed and V/f control purposes are clarified in closed and open loops of IM drives. The operations, benefits, and drawbacks of the direct and indirect field-oriented control systems are illustrated. Furthermore, the direct torque control (DTC) method for IMs is reviewed. Numerous VC methods established along with microprocessor/digital control, model reference adaptive control (MRAC), sliding mode control (SMC), and intelligent control (in terms of fuzzy logic (FL) and artificial neural networks (ANNs)) are described and examined. Uncertainties in the IM parameter are a considerable problem in VC drives. Therefore, this problem is addressed, and some studies that attempted to provide solutions are listed. Magnetic saturation and core loss impact are mentioned, as they are important issues in IM drives. Toward demonstrating the strengths and limitations of various VC configurations, a few experiments were simulated via MATLAB ® and Simulink ® that show the influence of machine parameter variation. Efforts are made to supply powerful guidelines for practicing engineers and researchers in AC drives.

Suggested Citation

  • Ahmed G. Mahmoud A. Aziz & Almoataz Y. Abdelaziz & Ziad M. Ali & Ahmed A. Zaki Diab, 2023. "A Comprehensive Examination of Vector-Controlled Induction Motor Drive Techniques," Energies, MDPI, vol. 16(6), pages 1-32, March.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:6:p:2854-:d:1101623
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    References listed on IDEAS

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    1. Fengxiang Wang & Zhenbin Zhang & Xuezhu Mei & José Rodríguez & Ralph Kennel, 2018. "Advanced Control Strategies of Induction Machine: Field Oriented Control, Direct Torque Control and Model Predictive Control," Energies, MDPI, vol. 11(1), pages 1-13, January.
    2. Youpeng Chen & Wenshao Bu & Yanke Qiao, 2021. "Research on the Speed Sliding Mode Observation Method of a Bearingless Induction Motor," Energies, MDPI, vol. 14(4), pages 1-18, February.
    3. Teresa Orlowska-Kowalska & Mateusz Korzonek & Grzegorz Tarchala, 2020. "Performance Analysis of Speed-Sensorless Induction Motor Drive Using Discrete Current-Error Based MRAS Estimators," Energies, MDPI, vol. 13(10), pages 1-23, May.
    4. Muhammad H. Iftikhar & Byung-Gun Park & Ji-Won Kim, 2021. "Design and Analysis of a Five-Phase Permanent-Magnet Synchronous Motor for Fault-Tolerant Drive," Energies, MDPI, vol. 14(2), pages 1-17, January.
    5. Ahmed G. Mahmoud A. Aziz & Hegazy Rez & Ahmed A. Zaki Diab, 2021. "Robust Sensorless Model-Predictive Torque Flux Control for High-Performance Induction Motor Drives," Mathematics, MDPI, vol. 9(4), pages 1-27, February.
    6. Xia, Yude & Wang, Jing & Meng, Bo & Chen, Xiangyong, 2020. "Further results on fuzzy sampled-data stabilization of chaotic nonlinear systems," Applied Mathematics and Computation, Elsevier, vol. 379(C).
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    Cited by:

    1. Marcin Kaminski & Tomasz Tarczewski, 2023. "Neural Network Applications in Electrical Drives—Trends in Control, Estimation, Diagnostics, and Construction," Energies, MDPI, vol. 16(11), pages 1-25, May.

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