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Induction Motor Drive Direct Torque Control and Predictive Torque Control Comparison Based on Switching Pattern Analysis

Author

Listed:
  • Pavel Karlovsky

    (Department of Electric Drives and Traction, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, 16627, Czech Republic)

  • Jiri Lettl

    (Department of Electric Drives and Traction, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, 16627, Czech Republic)

Abstract

This paper describes a switching pattern generated in case of induction motor drive predictive torque control (PTC) compared to a switching pattern of direct torque control (DTC). PTC is a modern control method for electric drives based on model predictive control (MPC). DTC is a very powerful method and is today an industrial standard for controlling an induction motor drive. Its usage is wide-spread, mainly in high-power applications. However, the method suffers from a few disadvantages. One of the causes of the control method’s problematic behavior is choosing the switching combinations in the flux sector. Another inconvenience is the common selection table not including all voltage vectors in given sector. By these factors, the ripples of flux vector trajectory and torque waveforms are influenced. The longer the sample time is, the more significant the influence of factors becomes, because only a few steps occur within one turn of the magnetic flux vector. Based on the detailed analysis, the reasons of the different performance of both systems are explained. The analysis performed by simulation in Matlab Simulink environment has proved that, while DTC might choose voltage vector that pushes system away from the reference values, the MPC always chooses the most proper vector. The experimental results measured on the real drive confirm the appropriate vector selection, just in case of the predictive control method.

Suggested Citation

  • Pavel Karlovsky & Jiri Lettl, 2018. "Induction Motor Drive Direct Torque Control and Predictive Torque Control Comparison Based on Switching Pattern Analysis," Energies, MDPI, vol. 11(7), pages 1-14, July.
  • Handle: RePEc:gam:jeners:v:11:y:2018:i:7:p:1793-:d:156872
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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. Milutin Petronijević & Nebojša Mitrović & Vojkan Kostić & Bojan Banković, 2017. "An Improved Scheme for Voltage Sag Override in Direct Torque Controlled Induction Motor Drives," Energies, MDPI, vol. 10(5), pages 1-16, May.
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    2. Bowei Zou & Yougui Guo & Xi Xiao & Bowen Yang & Xiao Wang & Mingzhang Shi & Yulin Tu, 2020. "Performance Improvement of Matrix Converter Direct Torque Control System," Energies, MDPI, vol. 13(12), pages 1-17, June.
    3. Sergey Goolak & Viktor Tkachenko & Svitlana Sapronova & Vaidas Lukoševičius & Robertas Keršys & Rolandas Makaras & Artūras Keršys & Borys Liubarskyi, 2022. "Synthesis of the Current Controller of the Vector Control System for Asynchronous Traction Drive of Electric Locomotives," Energies, MDPI, vol. 15(7), pages 1-19, March.
    4. Chaymae Fahassa & Yassine Zahraoui & Mohammed Akherraz & Mohammed Kharrich & Ehab E. Elattar & Salah Kamel, 2022. "Induction Motor DTC Performance Improvement by Inserting Fuzzy Logic Controllers and Twelve-Sector Neural Network Switching Table," Mathematics, MDPI, vol. 10(9), pages 1-14, April.
    5. Mingmao Hu & Feng Yang & Yi Liu & Liang Wu, 2022. "Finite Control Set Model-Free Predictive Current Control of a Permanent Magnet Synchronous Motor," Energies, MDPI, vol. 15(3), pages 1-18, January.
    6. Alessandro Benevieri & Gianmarco Maragliano & Mario Marchesoni & Massimiliano Passalacqua & Luis Vaccaro, 2021. "Induction Motor Direct Torque Control with Synchronous PWM," Energies, MDPI, vol. 14(16), pages 1-17, August.

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