IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v232y2024ics0960148124011169.html
   My bibliography  Save this article

An asynchronous distributed optimal wake control scheme for suppressing fatigue load and increasing power extraction in wind farms

Author

Listed:
  • Bai, Guan
  • Feng, Yaojing
  • Ma, Zi-Qian
  • Li, Xueping

Abstract

The fatigue damage and power generation of wind farms (WFs) is related to their service lifetime and economic benefits. In this paper, an asynchronous distributed optimal wake control (OWC) scheme is proposed to suppress fatigue load and increase power extraction in doubly-fed induction generator (DFIG)-based wind farm (WF). To improve control flexibility, the generator torque and pitch angle of a wind turbine (WT) is simultaneously controlled. Then, considering the influence of wake effect, the wind speed sensitivity is used to combine with the states of WTs for formulating the model predictive control (MPC)-based optimal control objectives. To deal with the higher computational complexity of the aerodynamic interactions efficiently, an asynchronous distributed alternating direction method of multipliers (AD-ADMM) is designed for the control system. A layout of a 3 × 5 wake WF is considered in the case studies to validate the outstanding performance of the proposed scheme.

Suggested Citation

  • Bai, Guan & Feng, Yaojing & Ma, Zi-Qian & Li, Xueping, 2024. "An asynchronous distributed optimal wake control scheme for suppressing fatigue load and increasing power extraction in wind farms," Renewable Energy, Elsevier, vol. 232(C).
    • Handle: RePEc:eee:renene:v:232:y:2024:i:c:s0960148124011169
    DOI: 10.1016/j.renene.2024.121048
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148124011169
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2024.121048?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 search for a different version of it.

    References listed on IDEAS

    as
    1. Siniscalchi-Minna, Sara & Bianchi, Fernando D. & De-Prada-Gil, Mikel & Ocampo-Martinez, Carlos, 2019. "A wind farm control strategy for power reserve maximization," Renewable Energy, Elsevier, vol. 131(C), pages 37-44.
    2. Pimenta, Francisco & Ribeiro, Daniel & Román, Adela & Magalhães, Filipe, 2024. "Predictive model for fatigue evaluation of floating wind turbines validated with experimental data," Renewable Energy, Elsevier, vol. 223(C).
    3. Anagnostopoulos, Sokratis J. & Bauer, Jens & Clare, Mariana C.A. & Piggott, Matthew D., 2023. "Accelerated wind farm yaw and layout optimisation with multi-fidelity deep transfer learning wake models," Renewable Energy, Elsevier, vol. 218(C).
    4. Ling, Ziyan & Zhao, Zhenzhou & Liu, Yige & Liu, Huiwen & Ali, Kashif & Liu, Yan & Wen, Yifan & Wang, Dingding & Li, Shijun & Su, Chunhao, 2024. "Multi-objective layout optimization for wind farms based on non-uniformly distributed turbulence and a new three-dimensional multiple wake model," Renewable Energy, Elsevier, vol. 227(C).
    5. González-Longatt, F. & Wall, P. & Terzija, V., 2012. "Wake effect in wind farm performance: Steady-state and dynamic behavior," Renewable Energy, Elsevier, vol. 39(1), pages 329-338.
    6. Ciri, Umberto & Rotea, Mario A. & Leonardi, Stefano, 2017. "Model-free control of wind farms: A comparative study between individual and coordinated extremum seeking," Renewable Energy, Elsevier, vol. 113(C), pages 1033-1045.
    7. Ansari, Quaiyum M. & Sánchez, Fernando & Mishnaevsky, Leon & Young, Trevor M., 2024. "Evaluation of offshore wind turbine blades coating thickness effect on leading edge protection system subject to rain erosion," Renewable Energy, Elsevier, vol. 226(C).
    8. Yao, Qi & Hu, Yang & Deng, Hui & Luo, Zhiling & Liu, Jizhen, 2020. "Two-degree-of-freedom active power control of megawatt wind turbine considering fatigue load optimization," Renewable Energy, Elsevier, vol. 162(C), pages 2096-2112.
    9. Dao, My Ha & Le, Quang Tuyen & Zhao, Xiang & Ooi, Chin Chun & Duong, Luu Trung Pham & Raghavan, Nagarajan, 2024. "Modelling of aero-mechanical response of wind turbine blade with damages by computational fluid dynamics, finite element analysis and Bayesian network," Renewable Energy, Elsevier, vol. 227(C).
    10. Lin, Mou & Porté-Agel, Fernando, 2024. "Wake meandering of wind turbines under dynamic yaw control and impacts on power and fatigue," Renewable Energy, Elsevier, vol. 223(C).
    11. Yan, Jie & Nuertayi, Akejiang & Yan, Yamin & Liu, Shan & Liu, Yongqian, 2023. "Hybrid physical and data driven modeling for dynamic operation characteristic simulation of wind turbine," Renewable Energy, Elsevier, vol. 215(C).
    12. Wang, Tengyuan & Cai, Chang & Liu, Junbo & Peng, Chaoyi & Wang, Yibo & Sun, Xiangyu & Zhong, Xiaohui & Zhang, Jingjing & Li, Qingan, 2024. "Wake characteristics and vortex structure evolution of floating offshore wind turbine under surge motion," Energy, Elsevier, vol. 302(C).
    13. Yao, Qi & Hu, Yang & Zhao, Tianyang & Guan, Yuanpeng & Luo, Zhiling & Liu, Jizhen, 2022. "Fatigue load suppression during active power control process in wind farm using dynamic-local-reference DMPC," Renewable Energy, Elsevier, vol. 183(C), pages 423-434.
    14. Liu, Ding Peng & Ferri, Giulio & Heo, Taemin & Marino, Enzo & Manuel, Lance, 2024. "On long-term fatigue damage estimation for a floating offshore wind turbine using a surrogate model," Renewable Energy, Elsevier, vol. 225(C).
    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. Dong, Xinghui & Li, Jia & Gao, Di & Zheng, Kai, 2021. "Wind speed modeling for cascade clusters of wind turbines Part 2: Wind speed reduction and aggregation superposition," Energy, Elsevier, vol. 215(PB).
    2. Del Pozo González, Héctor & Domínguez-García, José Luis, 2022. "Non-centralized hierarchical model predictive control strategy of floating offshore wind farms for fatigue load reduction," Renewable Energy, Elsevier, vol. 187(C), pages 248-256.
    3. Siniscalchi-Minna, Sara & Bianchi, Fernando D. & Ocampo-Martinez, Carlos & Domínguez-García, Jose Luis & De Schutter, Bart, 2020. "A non-centralized predictive control strategy for wind farm active power control: A wake-based partitioning approach," Renewable Energy, Elsevier, vol. 150(C), pages 656-669.
    4. Jäger, Tobias & McKenna, Russell & Fichtner, Wolf, 2015. "Onshore wind energy in Baden-Württemberg: a bottom-up economic assessment of the socio-technical potential," Working Paper Series in Production and Energy 7, Karlsruhe Institute of Technology (KIT), Institute for Industrial Production (IIP).
    5. Bizon, Nicu, 2019. "Efficient fuel economy strategies for the Fuel Cell Hybrid Power Systems under variable renewable/load power profile," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    6. Pasta, Edoardo & Faedo, Nicolás & Mattiazzo, Giuliana & Ringwood, John V., 2023. "Towards data-driven and data-based control of wave energy systems: Classification, overview, and critical assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 188(C).
    7. Croonenbroeck, Carsten & Møller Dahl, Christian, 2014. "Accurate medium-term wind power forecasting in a censored classification framework," Discussion Papers 351, European University Viadrina Frankfurt (Oder), Department of Business Administration and Economics.
    8. Li, Qing'an & Cai, Chang & Kamada, Yasunari & Maeda, Takao & Hiromori, Yuto & Zhou, Shuni & Xu, Jianzhong, 2021. "Prediction of power generation of two 30 kW Horizontal Axis Wind Turbines with Gaussian model," Energy, Elsevier, vol. 231(C).
    9. Xue, Jie & Yip, Tsz Leung & Wu, Bing & Wu, Chaozhong & van Gelder, P.H.A.J.M., 2021. "A novel fuzzy Bayesian network-based MADM model for offshore wind turbine selection in busy waterways: An application to a case in China," Renewable Energy, Elsevier, vol. 172(C), pages 897-917.
    10. Jong-Hyeon Shin & Jong-Hwi Lee & Se-Myong Chang, 2019. "A Simplified Numerical Model for the Prediction of Wake Interaction in Multiple Wind Turbines," Energies, MDPI, vol. 12(21), pages 1-14, October.
    11. Barbarelli, Silvio & Florio, Gaetano & Lo Zupone, Giacomo & Scornaienchi, Nino Michele, 2018. "First techno-economic evaluation of array configuration of self-balancing tidal kinetic turbines," Renewable Energy, Elsevier, vol. 129(PA), pages 183-200.
    12. Aju, Emmanuvel Joseph & Kumar, Devesh & Leffingwell, Melissa & Rotea, Mario A. & Jin, Yaqing, 2023. "The influence of yaw misalignment on turbine power output fluctuations and unsteady aerodynamic loads within wind farms," Renewable Energy, Elsevier, vol. 215(C).
    13. Siniscalchi-Minna, Sara & Bianchi, Fernando D. & De-Prada-Gil, Mikel & Ocampo-Martinez, Carlos, 2019. "A wind farm control strategy for power reserve maximization," Renewable Energy, Elsevier, vol. 131(C), pages 37-44.
    14. Song, Zhe & Zhang, Zijun & Chen, Xingying, 2016. "The decision model of 3-dimensional wind farm layout design," Renewable Energy, Elsevier, vol. 85(C), pages 248-258.
    15. Pérez Albornoz, C. & Escalante Soberanis, M.A. & Ramírez Rivera, V. & Rivero, M., 2022. "Review of atmospheric stability estimations for wind power applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 163(C).
    16. Sliz-Szkliniarz, B. & Eberbach, J. & Hoffmann, B. & Fortin, M., 2019. "Assessing the cost of onshore wind development scenarios: Modelling of spatial and temporal distribution of wind power for the case of Poland," Renewable and Sustainable Energy Reviews, Elsevier, vol. 109(C), pages 514-531.
    17. Ye, Shitong & Wang, Qiang & Mu, Yanfei & Luo, Kun & Fan, Jianren, 2024. "Loads and fatigue characteristics assessment of wind farm based on dynamic wake meandering model," Renewable Energy, Elsevier, vol. 236(C).
    18. van den Broek, Maarten J. & De Tavernier, Delphine & Sanderse, Benjamin & van Wingerden, Jan-Willem, 2022. "Adjoint optimisation for wind farm flow control with a free-vortex wake model," Renewable Energy, Elsevier, vol. 201(P1), pages 752-765.
    19. Soichiro Kiyoki & Shigeo Yoshida & Mostafa A. Rushdi, 2025. "Machine Learning-Based Prediction of 2 MW Wind Turbine Tower Loads During Power Production Based on Nacelle Behavior," Energies, MDPI, vol. 18(1), pages 1-26, January.
    20. Serrano González, Javier & Burgos Payán, Manuel & Santos, Jesús Manuel Riquelme & González-Longatt, Francisco, 2014. "A review and recent developments in the optimal wind-turbine micro-siting problem," Renewable and Sustainable Energy Reviews, Elsevier, vol. 30(C), pages 133-144.

    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:renene:v:232:y:2024:i:c:s0960148124011169. 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.journals.elsevier.com/renewable-energy .

    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.