IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v377y2025ipbs0306261924019019.html
   My bibliography  Save this article

Online assessment of frequency support capability of the DFIG-based wind farm using a knowledge and data-driven fusion Koopman method

Author

Listed:
  • Ruan, Yimin
  • Yao, Wei
  • Zong, Qihang
  • Zhou, Hongyu
  • Gan, Wei
  • Zhang, Xinhao
  • Li, Shaolin
  • Wen, Jinyu

Abstract

The increasing integration of renewable energy in power systems causes a decrease in the frequency stability of the system. Consequently, renewable energy stations, such as wind farms (WFs), must possess adequate frequency support capabilities. To maximize the frequency support capability of the WF, it is crucial to determine the frequency support capability boundaries (FSCB) of the WF. Due to the uneven distribution of wind resources and complex operating states of wind turbines, accurate evaluation of the FSCB of the WF is challenging. To address this issue, this paper proposes a knowledge and data-driven fusion Koopman method to assess the FSCB of the doubly fed induction generator (DFIG)-based WF. The characteristics of FSCB are analyzed and a multi-dimensional indicator system is defined to precisely quantify FSCB at both theoretical and practical levels. To accurately calculate the defined indicators, a knowledge and data-driven fusion method based on Koopman-mixed integer linear programming (MILP) is proposed. The knowledge of WF frequency regulation structures is integrated to construct Koopman dictionary functions. This allows the training of historical frequency regulation data to obtain the global linearized Koopman operator for the assessment object. Subsequently, it facilitates online assessment results using real-time data. Case studies are undertaken on the four-machine two-area power system including a DFIG-based WF. The assessment error of the proposed Koopman-MILP method is within 2%, with an assessment speed nearly 10 times faster than conventional nonlinear methods. The proposed dictionary function, compared to the one without integrated knowledge, improves assessment accuracy by nearly 5 times. Additionally, it reveals the impact of frequency regulation strategies, safety operation constraints, and wind resources on FSCB. Simulation results validate the rationality of the proposed indicators, the accuracy of the assessment method, and the practicality of the assessment outcomes under various operating conditions.

Suggested Citation

  • Ruan, Yimin & Yao, Wei & Zong, Qihang & Zhou, Hongyu & Gan, Wei & Zhang, Xinhao & Li, Shaolin & Wen, Jinyu, 2025. "Online assessment of frequency support capability of the DFIG-based wind farm using a knowledge and data-driven fusion Koopman method," Applied Energy, Elsevier, vol. 377(PB).
    • Handle: RePEc:eee:appene:v:377:y:2025:i:pb:s0306261924019019
    DOI: 10.1016/j.apenergy.2024.124518
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261924019019
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2024.124518?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Liu, Fa & Sun, Fubao & Wang, Xunming, 2023. "Impact of turbine technology on wind energy potential and CO2 emission reduction under different wind resource conditions in China," Applied Energy, Elsevier, vol. 348(C).
    2. Ju, Liwei & Tan, Zhongfu & Yuan, Jinyun & Tan, Qingkun & Li, Huanhuan & Dong, Fugui, 2016. "A bi-level stochastic scheduling optimization model for a virtual power plant connected to a wind–photovoltaic–energy storage system considering the uncertainty and demand response," Applied Energy, Elsevier, vol. 171(C), pages 184-199.
    3. Sun, Jingbo & Wang, Yang & He, Yuan & Cui, Wenrui & Chao, Qingchen & Shan, Baoguo & Wang, Zheng & Yang, Xiaofan, 2024. "The energy security risk assessment of inefficient wind and solar resources under carbon neutrality in China," Applied Energy, Elsevier, vol. 360(C).
    4. Bo Xu & Linwei Zhang & Yin Yao & Xiangdong Yu & Yixin Yang & Dongdong Li, 2021. "Virtual Inertia Coordinated Allocation Method Considering Inertia Demand and Wind Turbine Inertia Response Capability," Energies, MDPI, vol. 14(16), pages 1-15, August.
    5. Jiang, Boyou & Guo, Chuangxin & Chen, Zhe, 2024. "Frequency constrained unit commitment considering reserve provision of wind power," Applied Energy, Elsevier, vol. 361(C).
    6. Ratnam, Kamala Sarojini & Palanisamy, K. & Yang, Guangya, 2020. "Future low-inertia power systems: Requirements, issues, and solutions - A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(C).
    7. Li, Zhihao & Yang, Lun & Xu, Yinliang, 2023. "A dynamics-constrained method for distributed frequency regulation in low-inertia power systems," Applied Energy, Elsevier, vol. 344(C).
    8. Tavakol Aghaei, Vahid & Ağababaoğlu, Arda & Bawo, Biram & Naseradinmousavi, Peiman & Yıldırım, Sinan & Yeşilyurt, Serhat & Onat, Ahmet, 2023. "Energy optimization of wind turbines via a neural control policy based on reinforcement learning Markov chain Monte Carlo algorithm," Applied Energy, Elsevier, vol. 341(C).
    9. Yuan, Hong & Ma, Minda & Zhou, Nan & Xie, Hui & Ma, Zhili & Xiang, Xiwang & Ma, Xin, 2024. "Battery electric vehicle charging in China: Energy demand and emissions trends in the 2020s," Applied Energy, Elsevier, vol. 365(C).
    10. Zhang, Ning & Hu, Zhaoguang & Shen, Bo & Dang, Shuping & Zhang, Jian & Zhou, Yuhui, 2016. "A source–grid–load coordinated power planning model considering the integration of wind power generation," Applied Energy, Elsevier, vol. 168(C), pages 13-24.
    11. Ding, Zhetong & Li, Yaping & Zhang, Kaifeng & Peng, Jimmy Chih-Hsien, 2024. "Two-stage dynamic aggregation involving flexible resource composition and coordination based on submodular optimization," Applied Energy, Elsevier, vol. 360(C).
    12. Al kez, Dlzar & Foley, Aoife M. & McIlwaine, Neil & Morrow, D. John & Hayes, Barry P. & Zehir, M. Alparslan & Mehigan, Laura & Papari, Behnaz & Edrington, Chris S. & Baran, Mesut, 2020. "A critical evaluation of grid stability and codes, energy storage and smart loads in power systems with wind generation," Energy, Elsevier, vol. 205(C).
    13. Dai, Juchuan & Tan, Yayi & Shen, Xiangbin, 2019. "Investigation of energy output in mountain wind farm using multiple-units SCADA data," Applied Energy, Elsevier, vol. 239(C), pages 225-238.
    14. Li, Siyi & Zhang, Mingrui & Piggott, Matthew D., 2023. "End-to-end wind turbine wake modelling with deep graph representation learning," Applied Energy, Elsevier, vol. 339(C).
    15. Sun, Haiying & Qiu, Changyu & Lu, Lin & Gao, Xiaoxia & Chen, Jian & Yang, Hongxing, 2020. "Wind turbine power modelling and optimization using artificial neural network with wind field experimental data," Applied Energy, Elsevier, vol. 280(C).
    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. Ryosuke Shikuma & Dai Orihara & Hiroshi Kikusato & Akihisa Kaneko & Hisao Taoka & Yasuhiro Hayashi, 2025. "Quantitative Difference Between the Effective Inertia and Set Inertia Parameter of Virtual Synchronous Generators," Energies, MDPI, vol. 18(7), pages 1-23, March.

    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. Sebastian, Oliva H. & Carlos, Bahamonde D., 2024. "Trade-off between frequency stability and renewable generation – Studying virtual inertia from solar PV and operating stability constraints," Renewable Energy, Elsevier, vol. 232(C).
    2. Moss, Coleman & Maulik, Romit & Iungo, Giacomo Valerio, 2024. "Augmenting insights from wind turbine data through data-driven approaches," Applied Energy, Elsevier, vol. 376(PA).
    3. Lu, Hongkun & Gao, Xiaoxia & Yu, Jinxiao & Zhao, Qiansheng & Zhu, Xiaoxun & Ma, Wanli & Cao, Jingyuan & Wang, Yu, 2025. "Analysis and prediction of incoming wind speed for turbines in complex wind farm: Accounting for meteorological factors and spatiotemporal characteristics of wind farm," Applied Energy, Elsevier, vol. 381(C).
    4. Li, Zhihao & Xu, Yinliang, 2025. "Pricing balancing ancillary services for low-inertia power systems under uncertainty and nonconvexity," Applied Energy, Elsevier, vol. 377(PC).
    5. Cheng, Biyi & Du, Jianjun & Yao, Yingxue, 2022. "Machine learning methods to assist structure design and optimization of Dual Darrieus Wind Turbines," Energy, Elsevier, vol. 244(PA).
    6. Liu, Zhi-Feng & Zhao, Shi-Xiang & Luo, Xing-Fu & Huang, Ya-He & Gu, Rui-Zheng & Li, Ji-Xiang & Li, Ling-Ling, 2025. "Two-layer energy dispatching and collaborative optimization of regional integrated energy system considering stakeholders game and flexible load management," Applied Energy, Elsevier, vol. 379(C).
    7. Liu, Hui & Duan, Zhu & Chen, Chao, 2020. "Wind speed big data forecasting using time-variant multi-resolution ensemble model with clustering auto-encoder," Applied Energy, Elsevier, vol. 280(C).
    8. Rafiq Asghar & Francesco Riganti Fulginei & Hamid Wadood & Sarmad Saeed, 2023. "A Review of Load Frequency Control Schemes Deployed for Wind-Integrated Power Systems," Sustainability, MDPI, vol. 15(10), pages 1-29, May.
    9. Wang, Anqi & Pei, Yan & Qian, Zheng & Zareipour, Hamidreza & Jing, Bo & An, Jiayi, 2022. "A two-stage anomaly decomposition scheme based on multi-variable correlation extraction for wind turbine fault detection and identification," Applied Energy, Elsevier, vol. 321(C).
    10. Chen, Yuanbo & Zheng, Kedi & Feng, Cheng & Huang, Junling & Guo, Hongye & Zhong, Haiwang, 2025. "Optimal grid-forming BESS management incorporating internal battery physics," Applied Energy, Elsevier, vol. 385(C).
    11. Khasanzoda, Nasrullo & Safaraliev, Murodbek & Zicmane, Inga & Beryozkina, Svetlana & Rahimov, Jamshed & Ahyoev, Javod, 2022. "Use of smart grid based wind resources in isolated power systems," Energy, Elsevier, vol. 253(C).
    12. Zhu, Yongchao & Zhu, Caichao & Tan, Jianjun & Tan, Yong & Rao, Lei, 2022. "Anomaly detection and condition monitoring of wind turbine gearbox based on LSTM-FS and transfer learning," Renewable Energy, Elsevier, vol. 189(C), pages 90-103.
    13. Fabio Massaro & Maria Luisa Di Silvestre & Marco Ferraro & Francesco Montana & Eleonora Riva Sanseverino & Salvatore Ruffino, 2024. "Energy Hub Model for the Massive Adoption of Hydrogen in Power Systems," Energies, MDPI, vol. 17(17), pages 1-31, September.
    14. Keon Baek & Woong Ko & Jinho Kim, 2019. "Optimal Scheduling of Distributed Energy Resources in Residential Building under the Demand Response Commitment Contract," Energies, MDPI, vol. 12(14), pages 1-19, July.
    15. Zhu, Xiaoxun & Liu, Ruizhang & Chen, Yao & Gao, Xiaoxia & Wang, Yu & Xu, Zixu, 2021. "Wind speed behaviors feather analysis and its utilization on wind speed prediction using 3D-CNN," Energy, Elsevier, vol. 236(C).
    16. Chen, Yizhong & He, Li & Li, Jing, 2017. "Stochastic dominant-subordinate-interactive scheduling optimization for interconnected microgrids with considering wind-photovoltaic-based distributed generations under uncertainty," Energy, Elsevier, vol. 130(C), pages 581-598.
    17. Alain Aoun & Mehdi Adda & Adrian Ilinca & Mazen Ghandour & Hussein Ibrahim, 2024. "Optimizing Virtual Power Plant Management: A Novel MILP Algorithm to Minimize Levelized Cost of Energy, Technical Losses, and Greenhouse Gas Emissions," Energies, MDPI, vol. 17(16), pages 1-23, August.
    18. Yunlong Zhang & Panhong Zhang & Sheng Du & Hanlin Dong, 2024. "Economic Optimal Scheduling of Integrated Energy System Considering Wind–Solar Uncertainty and Power to Gas and Carbon Capture and Storage," Energies, MDPI, vol. 17(11), pages 1-26, June.
    19. Nie, Qingyun & Zhang, Lihui & Tong, Zihao & Dai, Guyu & Chai, Jianxue, 2022. "Cost compensation method for PEVs participating in dynamic economic dispatch based on carbon trading mechanism," Energy, Elsevier, vol. 239(PA).
    20. Long, Huan & Xu, Shaohui & Gu, Wei, 2022. "An abnormal wind turbine data cleaning algorithm based on color space conversion and image feature detection," Applied Energy, Elsevier, vol. 311(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:eee:appene:v:377:y:2025:i:pb:s0306261924019019. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.