IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v7y2014i11p7330-7347d42344.html
   My bibliography  Save this article

Electronic Power Transformer Control Strategy in Wind Energy Conversion Systems for Low Voltage Ride-through Capability Enhancement of Directly Driven Wind Turbines with Permanent Magnet Synchronous Generators (D-PMSGs)

Author

Listed:
  • Hui Huang

    (School of Electrical & Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China)

  • Chengxiong Mao

    (School of Electrical & Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China)

  • Jiming Lu

    (School of Electrical & Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China)

  • Dan Wang

    (School of Electrical & Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China)

Abstract

This paper investigates the use of an Electronic Power Transformer (EPT) incorporated with an energy storage system to smooth the wind power fluctuations and enhance the low voltage ride-through (LVRT) capability of directly driven wind turbines with permanent magnet synchronous generators (D-PMSGs). The decoupled control schemes of the system, including the grid side converter control scheme, generator side converter control scheme and the control scheme of the energy storage system, are presented in detail. Under normal operating conditions, the energy storage system absorbs the high frequency component of the D-PMSG output power to smooth the wind power fluctuations. Under grid fault conditions, the energy storage system absorbs the redundant power, which could not be transferred to the grid by the EPT, to help the D-PMSG to ride through low voltage conditions. This coordinated control strategy is validated by simulation studies using MATLAB/Simulink. With the proposed control strategy, the output wind power quality is improved and the D-PMSG can ride through severe grid fault conditions.

Suggested Citation

  • Hui Huang & Chengxiong Mao & Jiming Lu & Dan Wang, 2014. "Electronic Power Transformer Control Strategy in Wind Energy Conversion Systems for Low Voltage Ride-through Capability Enhancement of Directly Driven Wind Turbines with Permanent Magnet Synchronous G," Energies, MDPI, vol. 7(11), pages 1-18, November.
  • Handle: RePEc:gam:jeners:v:7:y:2014:i:11:p:7330-7347:d:42344
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/7/11/7330/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/7/11/7330/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zixia Sang & Chengxiong Mao & Jiming Lu & Dan Wang, 2013. "Analysis and Simulation of Fault Characteristics of Power Switch Failures in Distribution Electronic Power Transformers," Energies, MDPI, vol. 6(8), pages 1-23, August.
    2. Andrés Felipe Obando-Montaño & Camilo Carrillo & José Cidrás & Eloy Díaz-Dorado, 2014. "A STATCOM with Supercapacitors for Low-Voltage Ride-Through in Fixed-Speed Wind Turbines," Energies, MDPI, vol. 7(9), pages 1-31, September.
    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. Pei Huang & Chengxiong Mao & Dan Wang, 2017. "Electric Field Simulations and Analysis for High Voltage High Power Medium Frequency Transformer," Energies, MDPI, vol. 10(3), pages 1-11, March.
    2. Victor F. Mendes & Frederico F. Matos & Silas Y. Liu & Allan F. Cupertino & Heverton A. Pereira & Clodualdo V. De Sousa, 2016. "Low Voltage Ride-Through Capability Solutions for Permanent Magnet Synchronous Wind Generators," Energies, MDPI, vol. 9(1), pages 1-19, January.
    3. Mohamed Abdelrahem & Ralph Kennel, 2016. "Fault-Ride through Strategy for Permanent-Magnet Synchronous Generators in Variable-Speed Wind Turbines," Energies, MDPI, vol. 9(12), pages 1-15, December.
    4. Jae Woong Shim & Heejin Kim & Kyeon Hur, 2019. "Incorporating State-of-Charge Balancing into the Control of Energy Storage Systems for Smoothing Renewable Intermittency," Energies, MDPI, vol. 12(7), pages 1-13, March.
    5. Xiangwu Yan & Linlin Yang & Tiecheng Li, 2021. "The LVRT Control Scheme for PMSG-Based Wind Turbine Generator Based on the Coordinated Control of Rotor Overspeed and Supercapacitor Energy Storage," Energies, MDPI, vol. 14(2), pages 1-22, January.

    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. Pawel Szczesniak, 2019. "Challenges and Design Requirements for Industrial Applications of AC/AC Power Converters without DC-Link," Energies, MDPI, vol. 12(8), pages 1-18, April.
    2. Hongqian Wei & Youtong Zhang & Lei Yu & Mengzhu Zhang & Khaled Teffah, 2018. "A New Diagnostic Algorithm for Multiple IGBTs Open Circuit Faults by the Phase Currents for Power Inverter in Electric Vehicles," Energies, MDPI, vol. 11(6), pages 1-14, June.
    3. María Reveles-Miranda & Diego Fernando Sánchez-Flórez & José Ricardo Cruz-Chan & Eduardo Ernesto Ordoñez-López & Manuel Flota-Bañuelos & Daniella Pacheco-Catalán, 2018. "The Control Scheme of the Multifunction Inverter for Power Factor Improvement," Energies, MDPI, vol. 11(7), pages 1-17, June.
    4. Antonio Camacho & Miguel Castilla & Franco Canziani & Carlos Moreira & Paulo Coelho & Mario Gomes & Pedro E. Mercado, 2017. "Performance Comparison of Grid-Faulty Control Schemes for Inverter-Based Industrial Microgrids," Energies, MDPI, vol. 10(12), pages 1-25, December.
    5. Yanjian Peng & Yong Li & Zhisheng Xu & Ming Wen & Longfu Luo & Yijia Cao & Zbigniew Leonowicz, 2016. "Power Quality Improvement and LVRT Capability Enhancement of Wind Farms by Means of an Inductive Filtering Method," Energies, MDPI, vol. 9(4), pages 1-18, April.
    6. Lefeng Cheng & Tao Yu, 2018. "Dissolved Gas Analysis Principle-Based Intelligent Approaches to Fault Diagnosis and Decision Making for Large Oil-Immersed Power Transformers: A Survey," Energies, MDPI, vol. 11(4), pages 1-69, April.
    7. Minghui Ou & Hua Wei & Yiyi Zhang & Jiancheng Tan, 2019. "A Dynamic Adam Based Deep Neural Network for Fault Diagnosis of Oil-Immersed Power Transformers," Energies, MDPI, vol. 12(6), pages 1-16, March.
    8. Minh Quan Duong & Francesco Grimaccia & Sonia Leva & Marco Mussetta & Kim Hung Le, 2015. "Improving Transient Stability in a Grid-Connected Squirrel-Cage Induction Generator Wind Turbine System Using a Fuzzy Logic Controller," Energies, MDPI, vol. 8(7), pages 1-22, June.

    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:7:y:2014:i:11:p:7330-7347:d:42344. 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.