IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v11y2018i7p1743-d155979.html
   My bibliography  Save this article

A Dual-Voltage-Vector Model-Free Predictive Current Controller for Synchronous Reluctance Motor Drive Systems

Author

Listed:
  • Cheng-Kai Lin

    (Department of Electrical Engineering, National Taiwan Ocean University, Keelung 202, Taiwan)

  • Jen-te Yu

    (Department of Electrical Engineering, Chung Yuan Christian University, Taoyuan 320, Taiwan)

  • Hao-Qun Huang

    (Department of Electrical Engineering, National Taiwan Ocean University, Keelung 202, Taiwan)

  • Jyun-Ting Wang

    (Department of Electrical Engineering, National Taiwan Ocean University, Keelung 202, Taiwan)

  • Hsing-Cheng Yu

    (Department of Systems Engineering and Naval Architecture, National Taiwan Ocean University, Keelung 202, Taiwan)

  • Yen-Shin Lai

    (Department of Electrical Engineering, National Taipei University of Technology, Taipei 106, Taiwan)

Abstract

For current control in power conversion and motor drive systems, there exist three classic methods in the literature and they are the hysteresis current control (HCC), the sine pulse-width modulation (SPWM), and the space vector pulse width modulation (SVPWM). HCC is easy to implement, but has relatively large current harmonic distortion as the disadvantage. On the other hand, the SPWM and SVPWM use modulation technique, commonly together with at least one proportional-integral (PI) regulator to reduce load current ripples, and hence demanding more computation time. This paper aims to improve the performance of a recently proposed new current control method—the single-voltage-vector model predictive current control (SVV-MPCC), for synchronous reluctance motor (SynRMs) drives. To that end, a dual-voltage-vector model-free predictive current control (DVV-MFPCC) for SynRMs is proposed. Unlike the SVV-MPCC that applies only a single voltage vector per sampling period, the proposed DVV-MFPCC is capable of providing two successive segmentary current predictions in the next sampling period through all possible combinations from any two candidate switching states increasing the number of applicable switching modes from seven to nineteen and reducing the prediction error effectively. Moreover, the new control does not utilize any parameters of the SynRM nor its mathematical model. The performance is effectively enhanced compared to that of SVV-MPCC. The working principle of the DVV-MFPCC will be detailed in this paper. Finally, the SVV-MPCC, the single-voltage-vector model-free predictive current control (SVV-MFPCC), the dual-voltage-vector model predictive current control (DVV-MPCC), and the DVV-MFPCC are realized to control the stator currents of SynRM through a 32-bit microcontroller TMS320F28377S. Experimental results are provided to validate the new method and verify that the DVV-MFPCC performs better than do the SVV-MPCC, the SVV-MFPCC, and the DVV-MPCC.

Suggested Citation

  • Cheng-Kai Lin & Jen-te Yu & Hao-Qun Huang & Jyun-Ting Wang & Hsing-Cheng Yu & Yen-Shin Lai, 2018. "A Dual-Voltage-Vector Model-Free Predictive Current Controller for Synchronous Reluctance Motor Drive Systems," Energies, MDPI, vol. 11(7), pages 1-29, July.
  • Handle: RePEc:gam:jeners:v:11:y:2018:i:7:p:1743-:d:155979
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/11/7/1743/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/11/7/1743/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Vijay Kumar Singh & Ravi Nath Tripathi & Tsuyoshi Hanamoto, 2018. "HIL Co-Simulation of Finite Set-Model Predictive Control Using FPGA for a Three-Phase VSI System," Energies, MDPI, vol. 11(4), pages 1-15, April.
    2. Abdul Mannan Dadu & Saad Mekhilef & Tey Kok Soon & Mehdi Seyedmahmoudian & Ben Horan, 2017. "Near State Vector Selection-Based Model Predictive Control with Common Mode Voltage Mitigation for a Three-Phase Four-Leg Inverter," Energies, MDPI, vol. 10(12), pages 1-19, December.
    3. Ramon Guzmán & Luís García de Vicuña & Miguel Castilla & Jaume Miret & Antonio Camacho, 2017. "Finite Control Set Model Predictive Control for a Three-Phase Shunt Active Power Filter with a Kalman Filter-Based Estimation," Energies, MDPI, vol. 10(10), pages 1-14, October.
    4. Tien Hai Nguyen & Kyeong-Hwa Kim, 2017. "Finite Control Set–Model Predictive Control with Modulation to Mitigate Harmonic Component in Output Current for a Grid-Connected Inverter under Distorted Grid Conditions," Energies, MDPI, vol. 10(7), pages 1-25, July.
    5. Nan Jin & Leilei Guo & Gang Yao, 2017. "Model Predictive Direct Power Control for Nonredundant Fault Tolerant Grid-Connected Bidirectional Voltage Source Converter," Energies, MDPI, vol. 10(8), pages 1-16, August.
    6. Roh Chan & Sangshin Kwak, 2018. "Improved Finite-Control-Set Model Predictive Control for Cascaded H-Bridge Inverters," Energies, MDPI, vol. 11(2), pages 1-27, February.
    7. Jiefeng Hu & Ka Wai Eric Cheng, 2017. "Predictive Control of Power Electronics Converters in Renewable Energy Systems," Energies, MDPI, vol. 10(4), pages 1-14, April.
    8. Jose Miguel Espi & Jaime Castello, 2018. "Capacitive Emulation Using Predictive Current Control in LCL-Filtered Grid-Connected Converters to Mitigate Grid Current Distortion," Energies, MDPI, vol. 11(6), pages 1-15, June.
    9. Roh Chan & Sangshin Kwak, 2017. "Model-Based Predictive Current Control Method with Constant Switching Frequency for Single-Phase Voltage Source Inverters," Energies, MDPI, vol. 10(11), pages 1-21, November.
    10. Xiaoliang Yang & Guorong Liu & Anping Li & Le Van Dai, 2017. "A Predictive Power Control Strategy for DFIGs Based on a Wind Energy Converter System," Energies, MDPI, vol. 10(8), pages 1-24, July.
    11. 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.
    12. Dandan Su & Chengning Zhang & Yugang Dong, 2017. "An Improved Continuous-Time Model Predictive Control of Permanent Magnetic Synchronous Motors for a Wide-Speed Range," Energies, MDPI, vol. 10(12), pages 1-18, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Kang Wang & Ruituo Huai & Zhihao Yu & Xiaoyang Zhang & Fengjuan Li & Luwei Zhang, 2019. "Comparison Study of Induction Motor Models Considering Iron Loss for Electric Drives," Energies, MDPI, vol. 12(3), pages 1-13, February.
    2. Hui Cai & Hui Wang & Mengqiu Li & Shiqi Shen & Yaojing Feng & Jian Zheng, 2018. "Torque Ripple Reduction for Switched Reluctance Motor with Optimized PWM Control Strategy," Energies, MDPI, vol. 11(11), pages 1-27, November.
    3. Crestian Almazan Agustin & Jen-te Yu & Cheng-Kai Lin & Xiang-Yong Fu, 2019. "A Modulated Model Predictive Current Controller for Interior Permanent-Magnet Synchronous Motors," Energies, MDPI, vol. 12(15), pages 1-20, July.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ramon Guzmán & Luís García de Vicuña & Miguel Castilla & Jaume Miret & Antonio Camacho, 2017. "Finite Control Set Model Predictive Control for a Three-Phase Shunt Active Power Filter with a Kalman Filter-Based Estimation," Energies, MDPI, vol. 10(10), pages 1-14, October.
    2. Yuzhe Zhang & Xiaodong Liu & Haitao Li & Zhenbin Zhang, 2023. "A Model Independent Predictive Control of PMSG Wind Turbine Systems with a New Mechanism to Update Variables," Energies, MDPI, vol. 16(9), pages 1-15, April.
    3. Jaime A. Rohten & David N. Dewar & Pericle Zanchetta & Andrea Formentini & Javier A. Muñoz & Carlos R. Baier & José J. Silva, 2021. "Multivariable Deadbeat Control of Power Electronics Converters with Fast Dynamic Response and Fixed Switching Frequency," Energies, MDPI, vol. 14(2), pages 1-16, January.
    4. Yaqi Wang & Zhigang Liu, 2018. "Suppression Research Regarding Low-Frequency Oscillation in the Vehicle-Grid Coupling System Using Model-Based Predictive Current Control," Energies, MDPI, vol. 11(7), pages 1-21, July.
    5. Amit Kumer Podder & Md. Habibullah & Md. Tariquzzaman & Eklas Hossain & Sanjeevikumar Padmanaban, 2020. "Power Loss Analysis of Solar Photovoltaic Integrated Model Predictive Control Based On-Grid Inverter," Energies, MDPI, vol. 13(18), pages 1-26, September.
    6. Sofiane Bacha & Ramzi Saadi & Mohamed Yacine Ayad & Mohamed Sahraoui & Khaled Laadjal & Antonio J. Marques Cardoso, 2023. "Autonomous Electric-Vehicle Control Using Speed Planning Algorithm and Back-Stepping Approach," Energies, MDPI, vol. 16(5), pages 1-26, March.
    7. Gimara Rajapakse & Shantha Jayasinghe & Alan Fleming & Michael Negnevitsky, 2017. "A Model Predictive Control-Based Power Converter System for Oscillating Water Column Wave Energy Converters," Energies, MDPI, vol. 10(10), pages 1-17, October.
    8. Kodkin Vladimir & Anikin Alexander, 2021. "On the Physical Nature of Frequency Control Problems of Induction Motor Drives," Energies, MDPI, vol. 14(14), pages 1-15, July.
    9. Jin Zhu & Tongzhen Wei & Ming Ma & Libo Han, 2019. "Simple DC-Link Voltage Balancing Approach for Cascaded H-Bridge Rectifier with Asymmetric Parameters of Independent DC Loads," Energies, MDPI, vol. 12(9), pages 1-20, April.
    10. 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.
    11. Karol Wróbel & Piotr Serkies & Krzysztof Szabat, 2020. "Model Predictive Base Direct Speed Control of Induction Motor Drive—Continuous and Finite Set Approaches," Energies, MDPI, vol. 13(5), pages 1-15, March.
    12. Zhanqing Zhou & Xin Gu & Zhiqiang Wang & Guozheng Zhang & Qiang Geng, 2019. "An Improved Torque Control Strategy of PMSM Drive Considering On-Line MTPA Operation," Energies, MDPI, vol. 12(15), pages 1-17, July.
    13. Matthias Schiesser & Sébastien Wasterlain & Mario Marchesoni & Mauro Carpita, 2018. "A Simplified Design Strategy for Multi-Resonant Current Control of a Grid-Connected Voltage Source Inverter with an LCL Filter," Energies, MDPI, vol. 11(3), pages 1-15, March.
    14. Tadeusz Białoń & Roman Niestrój & Jarosław Michalak & Marian Pasko, 2021. "Induction Motor PI Observer with Reduced-Order Integrating Unit," Energies, MDPI, vol. 14(16), pages 1-12, August.
    15. Zhicheng Lin & Song Zheng & Zhicheng Chen & Rong Zheng & Wang Zhang, 2019. "Application Research of the Parallel System Theory and the Data Engine Approach in Wind Energy Conversion System," Energies, MDPI, vol. 12(5), pages 1-20, March.
    16. Jian Zhang & Yong Wan & Quan Ouyang & Meng Dong, 2023. "Nonlinear Stochastic Adaptive Control for DFIG-Based Wind Generation System," Energies, MDPI, vol. 16(15), pages 1-19, July.
    17. Crestian Almazan Agustin & Jen-te Yu & Cheng-Kai Lin & Xiang-Yong Fu, 2019. "A Modulated Model Predictive Current Controller for Interior Permanent-Magnet Synchronous Motors," Energies, MDPI, vol. 12(15), pages 1-20, July.
    18. Leonardo Comparatore & Magno Ayala & Yassine Kali & Jorge Rodas & Julio Pacher & Alfredo Renault & Raúl Gregor, 2023. "Discrete-Time Sliding Mode Current Control for a Seven-Level Cascade H-Bridge Converter," Energies, MDPI, vol. 16(5), pages 1-19, March.
    19. Angelo Lunardi & Eliomar R. Conde D & Jefferson de Assis & Darlan A. Fernandes & Alfeu J. Sguarezi Filho, 2021. "Model Predictive Control with Modulator Applied to Grid Inverter under Voltage Distorted," Energies, MDPI, vol. 14(16), pages 1-13, August.
    20. Rui Xiong & Suleiman M. Sharkh & Xi Zhang, 2018. "Research Progress on Electric and Intelligent Vehicles," Energies, MDPI, vol. 11(7), pages 1-5, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:11:y:2018:i:7:p:1743-:d:155979. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.