IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v392y2025ics0306261925007263.html

Model predictive control of wind turbine based on deep-dive holistic observer of tower top IMU

Author

Listed:
  • Wang, Yong
  • Zhu, Shanying
  • Deng, Ruiyu
  • Yang, Bo
  • Wang, Peng
  • Gu, Shuang

Abstract

In the context of cost reduction and efficiency improvement, with the continuous increase of single unit capacity of wind turbines, their units have become increasingly complex and fragile. The uncertainty of various factors makes wind turbines a highly uncertain nonlinear system, which poses many challenges to the research and control of wind turbines. Therefore, in order to ensure the safe, stable, and efficient operation of wind turbines, this paper proposes a model predictive control (MPC) method based on deep-deep holistic observer (DDHO) of tower top inertial measurement unit (IMU) to maximize power generation, alleviate fatigue loads on the turbines, and extend their service life. Firstly, tower top IMU sensor is used to measure parameters such as wind turbine rotor speed and acceleration. Then, by using DDHO combined with measurement parameters, the wind speed and unknown state of the wind turbine model are estimated. Then, by reducing phase lag in state estimation and adding dampers, the damping effect of the tower can be enhanced. Finally, the estimated parameters are used as inputs for MPC to perform real-time prediction processing on the predicted time-domain state values, in order to obtain the optimal control method for the system at the current time by solving the minimum objective function, minimizing the difference between the system reference trajectory and future output values. Through experiments of 10 MW wind turbine, it has been proven that the proposed control method can improve the power generation efficiency of wind turbines to a certain extent, reduce actuator wear, and reduce tower fore-aft fatigue loads.

Suggested Citation

  • Wang, Yong & Zhu, Shanying & Deng, Ruiyu & Yang, Bo & Wang, Peng & Gu, Shuang, 2025. "Model predictive control of wind turbine based on deep-dive holistic observer of tower top IMU," Applied Energy, Elsevier, vol. 392(C).
    • Handle: RePEc:eee:appene:v:392:y:2025:i:c:s0306261925007263
    DOI: 10.1016/j.apenergy.2025.125996
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261925007263
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2025.125996?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. Chen, Yao & Yan, Bowen & Yu, Meng & Huang, Guoqing & Qian, Guowei & Yang, Qingshan & Zhang, Kai & Mo, Ruiyu, 2025. "Wind tunnel study of wind turbine wake characteristics over two-dimensional hill considering the effects of terrain slope and turbine position," Applied Energy, Elsevier, vol. 380(C).
    2. Jong, Far Chen & Ahmed, Musse Mohamud, 2024. "Power line routing design by GIS-driven fuzzy traveling salesman problem-binary integer programming for green energy integration," Applied Energy, Elsevier, vol. 374(C).
    3. Keighobadi, Jafar & Mohammadian KhalafAnsar, Hadi & Naseradinmousavi, Peiman, 2022. "Adaptive neural dynamic surface control for uniform energy exploitation of floating wind turbine," Applied Energy, Elsevier, vol. 316(C).
    4. Xiangjie Liu & Le Feng & Xiaobing Kong, 2022. "A Comparative Study of Robust MPC and Stochastic MPC of Wind Power Generation System," Energies, MDPI, vol. 15(13), pages 1-22, June.
    5. Liu, Zhangmiaoge & Liu, Zhouxiao & Liu, Jianzhao & Wang, Ning, 2023. "Thermal management with fast temperature convergence based on optimized fuzzy PID algorithm for electric vehicle battery," Applied Energy, Elsevier, vol. 352(C).
    6. Li, Tenghui & Yang, Jin & Ioannou, Anastasia, 2024. "Data-driven control of wind turbine under online power strategy via deep learning and reinforcement learning," Renewable Energy, Elsevier, vol. 234(C).
    7. Dai, Juchuan & Yang, Xin & Wen, Li, 2018. "Development of wind power industry in China: A comprehensive assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 97(C), pages 156-164.
    8. Lin, Zi & Cevasco, Debora & Collu, Maurizio, 2020. "A methodology to develop reduced-order models to support the operation and maintenance of offshore wind turbines," Applied Energy, Elsevier, vol. 259(C).
    9. Yao, Qi & Tang, Jie & Ke, Yiming & Li, Li & Lu, Xiaoqin & Hu, Yang & Fang, Fang & Liu, Jizhen, 2024. "Anti-tropical cyclone load reduction control of wind turbines based on deep neural network yaw algorithm," Applied Energy, Elsevier, vol. 376(PB).
    10. Baisthakur, Shubham & Fitzgerald, Breiffni, 2024. "Physics-Informed Neural Network surrogate model for bypassing Blade Element Momentum theory in wind turbine aerodynamic load estimation," Renewable Energy, Elsevier, vol. 224(C).
    11. Deng, Jiewen & Xiao, Zhao & Zhao, Qiancheng & Zhan, Jun & Tao, Jie & Liu, Minghua & Song, Dongran, 2024. "Wind turbine short-term power forecasting method based on hybrid probabilistic neural network," Energy, Elsevier, vol. 313(C).
    12. Dhiman, Harsh S. & Deb, Dipankar & Foley, Aoife M., 2020. "Lidar assisted wake redirection in wind farms: A data driven approach," Renewable Energy, Elsevier, vol. 152(C), pages 484-493.
    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. Tang, Huakang & Wang, Honglei & Li, Chengjiang, 2025. "Time-varying cost modeling and maintenance strategy optimization of plateau wind turbines considering degradation states," Applied Energy, Elsevier, vol. 377(PA).
    2. Zeng, Huanze & Shi, Chenlu & Fang, Haoyu & Wu, Binrong, 2025. "Interpretable multivariate wind speed forecasting using sliding masked window-based decomposition and deep autoregressive networks," Energy, Elsevier, vol. 341(C).
    3. Liu, Weiwei & Song, Yifan & Bi, Kexin, 2021. "Exploring the patent collaboration network of China's wind energy industry: A study based on patent data from CNIPA," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    4. Cui, Qi & He, Ling & Han, Guoyi & Chen, Hao & Cao, Juanjuan, 2020. "Review on climate and water resource implications of reducing renewable power curtailment in China: A nexus perspective," Applied Energy, Elsevier, vol. 267(C).
    5. Karunakaran Venkatesan & Uma Govindarajan & Padmanathan Kasinathan & Sanjeevikumar Padmanaban & Jens Bo Holm-Nielsen & Zbigniew Leonowicz, 2019. "Economic Analysis of HRES Systems with Energy Storage During Grid Interruptions and Curtailment in Tamil Nadu, India: A Hybrid RBFNOEHO Technique," Energies, MDPI, vol. 12(16), pages 1-26, August.
    6. Bowen Yan & Shuangchen Tang & Meng Yu & Guowei Qian & Yao Chen, 2025. "Atmospheric Turbulence Effects on Wind Turbine Wakes over Two-Dimensional Hill: A Wind Tunnel Study," Energies, MDPI, vol. 18(11), pages 1-16, May.
    7. Lu, Hongfang & Ma, Xin & Huang, Kun & Azimi, Mohammadamin, 2020. "Prediction of offshore wind farm power using a novel two-stage model combining kernel-based nonlinear extension of the Arps decline model with a multi-objective grey wolf optimizer," Renewable and Sustainable Energy Reviews, Elsevier, vol. 127(C).
    8. Lin, Boqiang & Qiao, Qiao, 2023. "Exploring the participation willingness and potential carbon emission reduction of Chinese residential green electricity market," Energy Policy, Elsevier, vol. 174(C).
    9. Fan Zhang & Juchuan Dai & Deshun Liu & Linxing Li & Xin Long, 2019. "Investigation of the Pitch Load of Large-Scale Wind Turbines Using Field SCADA Data," Energies, MDPI, vol. 12(3), pages 1-20, February.
    10. Minan Tang & Wenjuan Wang & Jiandong Qiu & Detao Li & Linyuan Lei, 2022. "Active Power Cooperative Control for Wind Power Clusters with Multiple Temporal and Spatial Scales," Energies, MDPI, vol. 15(24), pages 1-21, December.
    11. Flavie Didier & Yong-Chao Liu & Salah Laghrouche & Daniel Depernet, 2024. "A Comprehensive Review on Advanced Control Methods for Floating Offshore Wind Turbine Systems above the Rated Wind Speed," Energies, MDPI, vol. 17(10), pages 1-33, May.
    12. Zhang, Zuowei & Teh, Jiashen, 2026. "Enhancing grid flexibility and renewable integration: A review of V2G and dynamic line rating synergies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 226(PB).
    13. Parsa, Seyed Masoud, 2025. "Physics-informed machine learning meets renewable energy systems: A review of advances, challenges, guidelines, and future outlooks," Applied Energy, Elsevier, vol. 402(PA).
    14. Mehrjoo, Azin & Tronci, Eleonora M. & Moaveni, Babak & Hines, Eric, 2025. "Hybrid surrogate input load estimation model in offshore wind turbines using transfer learning and multitask learning," Renewable Energy, Elsevier, vol. 247(C).
    15. Luo, Shihua & Hu, Weihao & Liu, Wen & Zhang, Zhenyuan & Bai, Chunguang & Huang, Qi & Chen, Zhe, 2022. "Study on the decarbonization in China's power sector under the background of carbon neutrality by 2060," Renewable and Sustainable Energy Reviews, Elsevier, vol. 166(C).
    16. Liu, Min & Qin, Jianjun & Lu, Da-Gang & Zhang, Wei-Heng & Zhu, Jiang-Sheng & Faber, Michael Havbro, 2022. "Towards resilience of offshore wind farms: A framework and application to asset integrity management," Applied Energy, Elsevier, vol. 322(C).
    17. Sun, Shilin & Wang, Tianyang & Yang, Hongxing & Chu, Fulei, 2022. "Damage identification of wind turbine blades using an adaptive method for compressive beamforming based on the generalized minimax-concave penalty function," Renewable Energy, Elsevier, vol. 181(C), pages 59-70.
    18. Qian, Xiaohang & Gao, Zhiteng & He, Yibin & Song, Leqi & Hu, Jian & Zhang, Lijun & Wang, Tongguang & Li, Ye, 2025. "Dynamic response analysis and blade stiffness sensitivity study of large floating offshore wind turbine with coupling effect," Renewable Energy, Elsevier, vol. 255(C).
    19. Duan, Guiyue & Gattari, Daniele & Porté-Agel, Fernando, 2025. "Theoretical and experimental study on power performance and wake characteristics of a floating wind turbine under pitch motion," Applied Energy, Elsevier, vol. 378(PA).
    20. 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).

    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:392:y:2025:i:c:s0306261925007263. 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.