IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i20p7191-d1264680.html
   My bibliography  Save this article

Refined Equivalent Modeling Method for Mixed Wind Farms Based on Small Sample Data

Author

Listed:
  • Qianlong Zhu

    (State Key Laboratory of Operation and Control of Renewable Energy & Storage Systems, China Electric Power Research Institute, Beijing 100192, China
    The School of Electrical Engineering and Automation, Anhui University, Hefei 230601, China)

  • Wenjing Xiong

    (The School of Electrical Engineering and Automation, Anhui University, Hefei 230601, China)

  • Haijiao Wang

    (State Key Laboratory of Operation and Control of Renewable Energy & Storage Systems, China Electric Power Research Institute, Beijing 100192, China)

  • Xiaoqiang Jin

    (The School of Electrical Engineering and Automation, Anhui University, Hefei 230601, China)

Abstract

For equivalent modeling of mixed wind farms (WFs), existing clustering indicators cannot consider the complex coupling characteristics between different types of wind turbines (WTs). In this paper, a refined equivalent modeling approach based on artificial intelligence technology is proposed. Firstly, the electromechanical transient performance of mixed WFs is analyzed. The WT type, wind speed and direction, and voltage dip are considered the dominant factors affecting the external dynamic response of mixed WFs. Secondly, the equivalent node model is established, including the selection of independent and dependent variables. Then, the multiple artificial neural networks (ANNs) are trained one by one based on small sample data, to fit the nonlinear relationship between the dependent variables and the independent variables. Finally, the dynamic response of the power systems with a mixed WF is simulated in the MATLAB platform. A comparison of the errors in electromechanical phenomena demonstrates that the proposed model can reflect the external characteristics of the test mixed WF in different wind conditions and voltage dips.

Suggested Citation

  • Qianlong Zhu & Wenjing Xiong & Haijiao Wang & Xiaoqiang Jin, 2023. "Refined Equivalent Modeling Method for Mixed Wind Farms Based on Small Sample Data," Energies, MDPI, vol. 16(20), pages 1-17, October.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:20:p:7191-:d:1264680
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/20/7191/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/20/7191/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Songjune Lee & Seungjin Kang & Gwang-Se Lee, 2023. "Predictions for Bending Strain at the Tower Bottom of Offshore Wind Turbine Based on the LSTM Model," Energies, MDPI, vol. 16(13), pages 1-18, June.
    2. Jiawei Wu & Jinyu Xiao & Jinming Hou & Xunyan Lyu, 2023. "Development Potential Assessment for Wind and Photovoltaic Power Energy Resources in the Main Desert–Gobi–Wilderness Areas of China," Energies, MDPI, vol. 16(12), pages 1-22, June.
    3. Qianlong Zhu & Ming Ding & Pingping Han, 2016. "Equivalent Modeling of DFIG-Based Wind Power Plant Considering Crowbar Protection," Mathematical Problems in Engineering, Hindawi, vol. 2016, pages 1-16, August.
    4. Abhinandan Routray & Yiza Srikanth Reddy & Sung-ho Hur, 2023. "Predictive Control of a Wind Turbine Based on Neural Network-Based Wind Speed Estimation," Sustainability, MDPI, vol. 15(12), pages 1-22, June.
    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. Agnieszka Dudziak & Arkadiusz Małek & Andrzej Marciniak & Jacek Caban & Jarosław Seńko, 2024. "Probabilistic Analysis of Green Hydrogen Production from a Mix of Solar and Wind Energy," Energies, MDPI, vol. 17(17), pages 1-22, September.

    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. Sile Hu & Yuan Gao & Yuan Wang & Yuan Yu & Yue Bi & Linfeng Cao & Muhammad Farhan Khan & Jiaqiang Yang, 2024. "Optimal Configuration of Wind–Solar–Thermal-Storage Power Energy Based on Dynamic Inertia Weight Chaotic Particle Swarm," Energies, MDPI, vol. 17(5), pages 1-14, February.
    2. Christopher Jung & Dirk Schindler, 2023. "Reasons for the Recent Onshore Wind Capacity Factor Increase," Energies, MDPI, vol. 16(14), pages 1-17, July.
    3. Qianlong Zhu & Jun Tao & Tianbai Deng & Mingxing Zhu, 2022. "A General Equivalent Modeling Method for DFIG Wind Farms Based on Data-Driven Modeling," Energies, MDPI, vol. 15(19), pages 1-14, September.
    4. Abhinandan Routray & Nitin Sivakumar & Sung-ho Hur & Deok-je Bang, 2023. "A Comparative Study of Optimal Individual Pitch Control Methods," Sustainability, MDPI, vol. 15(14), pages 1-25, 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:16:y:2023:i:20:p:7191-:d:1264680. 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.