IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i17p4361-d1468615.html
   My bibliography  Save this article

Parameter Identification of PMSG-Based Wind Turbine Based on Sensitivity Analysis and Improved Gray Wolf Optimization

Author

Listed:
  • Bingjie Zhai

    (College of Electrical Engineering, Sichuan University, Chengdu 610065, China)

  • Kaijian Ou

    (Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System, Guangzhou 510663, China)

  • Yuhong Wang

    (College of Electrical Engineering, Sichuan University, Chengdu 610065, China)

  • Tian Cao

    (College of Electrical Engineering, Sichuan University, Chengdu 610065, China)

  • Huaqing Dai

    (College of Electrical Engineering, Sichuan University, Chengdu 610065, China)

  • Zongsheng Zheng

    (College of Electrical Engineering, Sichuan University, Chengdu 610065, China)

Abstract

With the large-scale integration of wind power, it is essential to establish an electromagnetic transient (EMT) model of a wind turbine system. Focusing on the problem of the difficulty in obtaining the parameters of the direct-driven permanent magnet synchronous generator (PMSG) model, this manuscript proposes a method based on trajectory sensitivity analysis and improved gray wolf optimization (IGWO) for identifying the parameters of the PMSG EMT model. First, a model of a PMSG wind turbine is established on an EMT simulation platform. Then, the key parameters of the model are determined based on the sensitivity analysis. Five control parameters are selected as the key parameters for their higher sensitivity indexes. Finally, the key parameters are accurately identified, using the proposed IGWO algorithm. The final case study demonstrates that the proposed IGWO algorithm has better optimization performance compared with the GWO algorithm and particle swarm optimization (PSO) algorithm. In addition, the simulation waveforms show that the identified parameters are accurate and applicable to other operating conditions.

Suggested Citation

  • Bingjie Zhai & Kaijian Ou & Yuhong Wang & Tian Cao & Huaqing Dai & Zongsheng Zheng, 2024. "Parameter Identification of PMSG-Based Wind Turbine Based on Sensitivity Analysis and Improved Gray Wolf Optimization," Energies, MDPI, vol. 17(17), pages 1-15, August.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:17:p:4361-:d:1468615
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/17/4361/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/17/17/4361/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Junda Li & Oluleke Babayomi & Zhenbin Zhang & Zhen Li, 2023. "Robust Predictive Current Control of PMSG Wind Turbines with Sensor Noise Suppression," Energies, MDPI, vol. 16(17), pages 1-17, August.
    2. Fanjie Yang & Yun Zeng & Jing Qian & Youtao Li & Shihao Xie, 2023. "Parameter Identification of Doubly-Fed Induction Wind Turbine Based on the ISIAGWO Algorithm," Energies, MDPI, vol. 16(3), pages 1-19, January.
    3. Jia, Ke & Gu, Chenjie & Li, Lun & Xuan, Zhengwen & Bi, Tianshu & Thomas, David, 2018. "Sparse voltage amplitude measurement based fault location in large-scale photovoltaic power plants," Applied Energy, Elsevier, vol. 211(C), pages 568-581.
    Full references (including those not matched with items on IDEAS)

    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. Jiang Li & Wenzhen Wei & Shuo Zhang & Guoqing Li & Chenghong Gu, 2018. "Conditional Maximum Likelihood of Three-Phase Phasor Estimation for μPMU in Active Distribution Networks," Energies, MDPI, vol. 11(5), pages 1-18, May.
    2. Hong, Ying-Yi & Pula, Rolando A., 2022. "Detection and classification of faults in photovoltaic arrays using a 3D convolutional neural network," Energy, Elsevier, vol. 246(C).
    3. Su, Hongzhi & Wang, Chengshan & Li, Peng & Li, Peng & Liu, Zhelin & Wu, Jianzhong, 2019. "Novel voltage-to-power sensitivity estimation for phasor measurement unit-unobservable distribution networks based on network equivalent," Applied Energy, Elsevier, vol. 250(C), pages 302-312.
    4. Xiangyu Kong & Xiaoxiao Yuan & Yuting Wang & Yong Xu & Li Yu, 2019. "Research on Optimal D-PMU Placement Technology to Improve the Observability of Smart Distribution Networks," Energies, MDPI, vol. 12(22), pages 1-23, November.
    5. Fanjie Yang & Yun Zeng & Jing Qian & Youtao Li & Shihao Xie, 2023. "Parameter Identification of Doubly-Fed Induction Wind Turbine Based on the ISIAGWO Algorithm," Energies, MDPI, vol. 16(3), pages 1-19, January.
    6. Sapountzoglou, Nikolaos & Lago, Jesus & De Schutter, Bart & Raison, Bertrand, 2020. "A generalizable and sensor-independent deep learning method for fault detection and location in low-voltage distribution grids," Applied Energy, Elsevier, vol. 276(C).
    7. Zhao, Zhida & Yu, Hao & Li, Peng & Li, Peng & Kong, Xiangyu & Wu, Jianzhong & Wang, Chengshan, 2019. "Optimal placement of PMUs and communication links for distributed state estimation in distribution networks," Applied Energy, Elsevier, vol. 256(C).
    8. Dey, Maitreyee & Rana, Soumya Prakash & Simmons, Clarke V. & Dudley, Sandra, 2021. "Solar farm voltage anomaly detection using high-resolution μPMU data-driven unsupervised machine learning," Applied Energy, Elsevier, vol. 303(C).
    9. Sun, Chenhao & Wang, Xin & Zheng, Yihui, 2020. "An ensemble system to predict the spatiotemporal distribution of energy security weaknesses in transmission networks," Applied Energy, Elsevier, vol. 258(C).

    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:17:y:2024:i:17:p:4361-:d:1468615. 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.