IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v303y2021ics0306261921010515.html
   My bibliography  Save this article

Formulas of the optimized yaw angles for cooperative control of wind farms with aligned turbines to maximize the power production

Author

Listed:
  • Ma, Hongliang
  • Ge, Mingwei
  • Wu, Guangxing
  • Du, Bowen
  • Liu, Yongqian

Abstract

Cooperative yaw control of wind turbines can substantially increase the total power production of wind farms, especially for the ones with aligned turbines. However, a generalized and effective method to quickly determine the turbines’ yaw angles for the wind farm control is rather limited. To this end, a systematic study was carried out on the cooperative yaw control of a single column of turbines. It is found that the optimized yaw angles are not sensitive to the turbine number. All the turbines should yaw to the identical direction for power maximization except the last turbine for which no yaw is required. The cooperative yaw control strategy can be simplified to a solution space only containing two yaw angles, i.e., the yaw angle of the first turbine and the angle for the downstream yawed turbines. Then, two algebra formulas are proposed to quickly determine the two yaw angles for the wind farm control only using three available parameters, i.e., the thrust coefficient of the rotor, the wake decay coefficient and the turbine spacing. To validate the proposed formulas, high-fidelity large-eddy simulations are performed. The results demonstrate that the proposed control strategy can increase the power production significantly for cases with strong wake effects. For one of the cases with five aligned turbines, the total power can be enhanced by 17.5%.

Suggested Citation

  • Ma, Hongliang & Ge, Mingwei & Wu, Guangxing & Du, Bowen & Liu, Yongqian, 2021. "Formulas of the optimized yaw angles for cooperative control of wind farms with aligned turbines to maximize the power production," Applied Energy, Elsevier, vol. 303(C).
    • Handle: RePEc:eee:appene:v:303:y:2021:i:c:s0306261921010515
    DOI: 10.1016/j.apenergy.2021.117691
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261921010515
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2021.117691?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. Dou, Bingzheng & Guala, Michele & Lei, Liping & Zeng, Pan, 2019. "Wake model for horizontal-axis wind and hydrokinetic turbines in yawed conditions," Applied Energy, Elsevier, vol. 242(C), pages 1383-1395.
    2. Mou Lin & Fernando Porté-Agel, 2019. "Large-Eddy Simulation of Yawed Wind-Turbine Wakes: Comparisons with Wind Tunnel Measurements and Analytical Wake Models," Energies, MDPI, vol. 12(23), pages 1-18, November.
    3. Ge, Mingwei & Zhang, Shuaibin & Meng, Hang & Ma, Hongliang, 2020. "Study on interaction between the wind-turbine wake and the urban district model by large eddy simulation," Renewable Energy, Elsevier, vol. 157(C), pages 941-950.
    4. Fleming, Paul A. & Gebraad, Pieter M.O. & Lee, Sang & van Wingerden, Jan-Willem & Johnson, Kathryn & Churchfield, Matt & Michalakes, John & Spalart, Philippe & Moriarty, Patrick, 2014. "Evaluating techniques for redirecting turbine wakes using SOWFA," Renewable Energy, Elsevier, vol. 70(C), pages 211-218.
    5. Zhang, Huan & Ge, Mingwei & Liu, Yongqian & Yang, Xiang I.A., 2021. "A new coupled model for the equivalent roughness heights of wind farms," Renewable Energy, Elsevier, vol. 171(C), pages 34-46.
    6. Bansal, Jagdish Chand & Farswan, Pushpa, 2017. "Wind farm layout using biogeography based optimization," Renewable Energy, Elsevier, vol. 107(C), pages 386-402.
    7. Zhaobin Li & Xiaolei Yang, 2020. "Evaluation of Actuator Disk Model Relative to Actuator Surface Model for Predicting Utility-Scale Wind Turbine Wakes," Energies, MDPI, vol. 13(14), pages 1-18, July.
    8. Qian, Yaoru & Wang, Tongguang & Yuan, Yiping & Zhang, Yuquan, 2020. "Comparative study on wind turbine wakes using a modified partially-averaged Navier-Stokes method and large eddy simulation," Energy, Elsevier, vol. 206(C).
    9. Reddy, Sohail R., 2021. "An efficient method for modeling terrain and complex terrain boundaries in constrained wind farm layout optimization," Renewable Energy, Elsevier, vol. 165(P1), pages 162-173.
    10. Hou, Peng & Hu, Weihao & Soltani, Mohsen & Chen, Cong & Chen, Zhe, 2017. "Combined optimization for offshore wind turbine micro siting," Applied Energy, Elsevier, vol. 189(C), pages 271-282.
    11. Wilson, Dennis & Rodrigues, Silvio & Segura, Carlos & Loshchilov, Ilya & Hutter, Frank & Buenfil, Guillermo López & Kheiri, Ahmed & Keedwell, Ed & Ocampo-Pineda, Mario & Özcan, Ender & Peña, Sergio Iv, 2018. "Evolutionary computation for wind farm layout optimization," Renewable Energy, Elsevier, vol. 126(C), pages 681-691.
    12. Archer, Cristina L. & Vasel-Be-Hagh, Ahmadreza & Yan, Chi & Wu, Sicheng & Pan, Yang & Brodie, Joseph F. & Maguire, A. Eoghan, 2018. "Review and evaluation of wake loss models for wind energy applications," Applied Energy, Elsevier, vol. 226(C), pages 1187-1207.
    13. Park, Jinkyoo & Law, Kincho H., 2016. "A data-driven, cooperative wind farm control to maximize the total power production," Applied Energy, Elsevier, vol. 165(C), pages 151-165.
    14. Ge, Mingwei & Gayme, Dennice F. & Meneveau, Charles, 2021. "Large-eddy simulation of wind turbines immersed in the wake of a cube-shaped building," Renewable Energy, Elsevier, vol. 163(C), pages 1063-1077.
    15. Adaramola, M.S. & Krogstad, P.-Å., 2011. "Experimental investigation of wake effects on wind turbine performance," Renewable Energy, Elsevier, vol. 36(8), pages 2078-2086.
    16. Lopez, Daniel & Kuo, Jim & Li, Ni, 2019. "A novel wake model for yawed wind turbines," Energy, Elsevier, vol. 178(C), pages 158-167.
    17. Ge, Mingwei & Wu, Ying & Liu, Yongqian & Yang, Xiang I.A., 2019. "A two-dimensional Jensen model with a Gaussian-shaped velocity deficit," Renewable Energy, Elsevier, vol. 141(C), pages 46-56.
    18. Ge, Mingwei & Wu, Ying & Liu, Yongqian & Li, Qi, 2019. "A two-dimensional model based on the expansion of physical wake boundary for wind-turbine wakes," Applied Energy, Elsevier, vol. 233, pages 975-984.
    19. Kuo, Jim Y.J. & Romero, David A. & Beck, J. Christopher & Amon, Cristina H., 2016. "Wind farm layout optimization on complex terrains – Integrating a CFD wake model with mixed-integer programming," Applied Energy, Elsevier, vol. 178(C), pages 404-414.
    20. Hou, Peng & Enevoldsen, Peter & Hu, Weihao & Chen, Cong & Chen, Zhe, 2017. "Offshore wind farm repowering optimization," Applied Energy, Elsevier, vol. 208(C), pages 834-844.
    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. Song, Jeonghwan & Kim, Taewan & You, Donghyun, 2023. "Particle swarm optimization of a wind farm layout with active control of turbine yaws," Renewable Energy, Elsevier, vol. 206(C), pages 738-747.
    2. Shu, Tong & Song, Dongran & Joo, Young Hoon, 2022. "Non-centralised coordinated optimisation for maximising offshore wind farm power via a sparse communication architecture," Applied Energy, Elsevier, vol. 324(C).
    3. Mou Lin & Fernando Porté-Agel, 2023. "Power Production and Blade Fatigue of a Wind Turbine Array Subjected to Active Yaw Control," Energies, MDPI, vol. 16(6), pages 1-17, March.
    4. Song, Dongran & Shen, Xutao & Gao, Yang & Wang, Lei & Du, Xin & Xu, Zhiliang & Zhang, Zhihong & Huang, Chaoneng & Yang, Jian & Dong, Mi & Joo, Young Hoo, 2023. "Application of surrogate-assisted global optimization algorithm with dimension-reduction in power optimization of floating offshore wind farm," Applied Energy, Elsevier, vol. 351(C).
    5. Yang, Haoze & Ge, Mingwei & Gu, Bo & Du, Bowen & Liu, Yongqian, 2022. "The effect of swell on marine atmospheric boundary layer and the operation of an offshore wind turbine," Energy, Elsevier, vol. 244(PB).
    6. Yang, Haoze & Ge, Mingwei & Abkar, Mahdi & Yang, Xiang I.A., 2022. "Large-eddy simulation study of wind turbine array above swell sea," Energy, Elsevier, vol. 256(C).
    7. Zhang, Shuaibin & Du, Bowen & Ge, Mingwei & Zuo, Yingtao, 2022. "Study on the operation of small rooftop wind turbines and its effect on the wind environment in blocks," Renewable Energy, Elsevier, vol. 183(C), pages 708-718.
    8. Zhu, Xiaoxun & Chen, Yao & Xu, Shinai & Zhang, Shaohai & Gao, Xiaoxia & Sun, Haiying & Wang, Yu & Zhao, Fei & Lv, Tiancheng, 2023. "Three-dimensional non-uniform full wake characteristics for yawed wind turbine with LiDAR-based experimental verification," Energy, Elsevier, vol. 270(C).
    9. Wang, Yu & Wei, Shanbi & Yang, Wei & Chai, Yi, 2023. "Adaptive economic predictive control for offshore wind farm active yaw considering generation uncertainty," Applied Energy, Elsevier, vol. 351(C).
    10. Zheng, Yidan & Liu, Huiwen & Chamorro, Leonardo P. & Zhao, Zhenzhou & Li, Ye & Zheng, Yuan & Tang, Kexin, 2023. "Impact of turbulence level on intermittent-like events in the wake of a model wind turbine," Renewable Energy, Elsevier, vol. 203(C), pages 45-55.
    11. Li, Siyi & Robert, Arnaud & Faisal, A. Aldo & Piggott, Matthew D., 2024. "Learning to optimise wind farms with graph transformers," Applied Energy, Elsevier, vol. 359(C).
    12. Cai, Wei & Hu, Yang & Fang, Fang & Yao, Lujin & Liu, Jizhen, 2023. "Wind farm power production and fatigue load optimization based on dynamic partitioning and wake redirection of wind turbines," Applied Energy, Elsevier, vol. 339(C).
    13. Yu, Yang & Wu, Shibo & Yu, Jianxing & Xu, Ya & Song, Lin & Xu, Weipeng, 2022. "A hybrid multi-criteria decision-making framework for offshore wind turbine selection: A case study in China," Applied Energy, Elsevier, vol. 328(C).

    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. Gu, Bo & Meng, Hang & Ge, Mingwei & Zhang, Hongtao & Liu, Xinyu, 2021. "Cooperative multiagent optimization method for wind farm power delivery maximization," Energy, Elsevier, vol. 233(C).
    2. Shen, Wen Zhong & Lin, Jian Wei & Jiang, Yu Hang & Feng, Ju & Cheng, Li & Zhu, Wei Jun, 2023. "A novel yaw wake model for wind farm control applications," Renewable Energy, Elsevier, vol. 218(C).
    3. Fan, Xiantao & Ge, Mingwei & Tan, Wei & Li, Qi, 2021. "Impacts of coexisting buildings and trees on the performance of rooftop wind turbines: An idealized numerical study," Renewable Energy, Elsevier, vol. 177(C), pages 164-180.
    4. Li, Li & Huang, Zhi & Ge, Mingwei & Zhang, Qiying, 2022. "A novel three-dimensional analytical model of the added streamwise turbulence intensity for wind-turbine wakes," Energy, Elsevier, vol. 238(PB).
    5. Zhang, Huan & Ge, Mingwei & Liu, Yongqian & Yang, Xiang I.A., 2021. "A new coupled model for the equivalent roughness heights of wind farms," Renewable Energy, Elsevier, vol. 171(C), pages 34-46.
    6. Dou, Bingzheng & Guala, Michele & Lei, Liping & Zeng, Pan, 2019. "Wake model for horizontal-axis wind and hydrokinetic turbines in yawed conditions," Applied Energy, Elsevier, vol. 242(C), pages 1383-1395.
    7. Eidi, Ali & Ghiassi, Reza & Yang, Xiang & Abkar, Mahdi, 2021. "Model-form uncertainty quantification in RANS simulations of wakes and power losses in wind farms," Renewable Energy, Elsevier, vol. 179(C), pages 2212-2223.
    8. Amiri, Mojtaba Maali & Shadman, Milad & Estefen, Segen F., 2024. "A review of physical and numerical modeling techniques for horizontal-axis wind turbine wakes," Renewable and Sustainable Energy Reviews, Elsevier, vol. 193(C).
    9. Wu, Yan & Xia, Tianqi & Wang, Yufei & Zhang, Haoran & Feng, Xiao & Song, Xuan & Shibasaki, Ryosuke, 2022. "A synchronization methodology for 3D offshore wind farm layout optimization with multi-type wind turbines and obstacle-avoiding cable network," Renewable Energy, Elsevier, vol. 185(C), pages 302-320.
    10. Zhang, Jincheng & Zhao, Xiaowei, 2020. "A novel dynamic wind farm wake model based on deep learning," Applied Energy, Elsevier, vol. 277(C).
    11. Gao, Xiaoxia & Li, Bingbing & Wang, Tengyuan & Sun, Haiying & Yang, Hongxing & Li, Yonghua & Wang, Yu & Zhao, Fei, 2020. "Investigation and validation of 3D wake model for horizontal-axis wind turbines based on filed measurements," Applied Energy, Elsevier, vol. 260(C).
    12. Zhang, Shaohai & Duan, Huanfeng & Lu, Lin & He, Ruiyang & Gao, Xiaoxia & Zhu, Songye, 2024. "Quantification of three-dimensional added turbulence intensity for the horizontal-axis wind turbine considering the wake anisotropy," Energy, Elsevier, vol. 294(C).
    13. Ge, Mingwei & Gayme, Dennice F. & Meneveau, Charles, 2021. "Large-eddy simulation of wind turbines immersed in the wake of a cube-shaped building," Renewable Energy, Elsevier, vol. 163(C), pages 1063-1077.
    14. Yang, Haoze & Ge, Mingwei & Gu, Bo & Du, Bowen & Liu, Yongqian, 2022. "The effect of swell on marine atmospheric boundary layer and the operation of an offshore wind turbine," Energy, Elsevier, vol. 244(PB).
    15. Zhang, Jincheng & Zhao, Xiaowei, 2022. "Wind farm wake modeling based on deep convolutional conditional generative adversarial network," Energy, Elsevier, vol. 238(PB).
    16. He, Ruiyang & Sun, Haiying & Gao, Xiaoxia & Yang, Hongxing, 2022. "Wind tunnel tests for wind turbines: A state-of-the-art review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 166(C).
    17. Lo Brutto, Ottavio A. & Guillou, Sylvain S. & Thiébot, Jérôme & Gualous, Hamid, 2017. "Assessing the effectiveness of a global optimum strategy within a tidal farm for power maximization," Applied Energy, Elsevier, vol. 204(C), pages 653-666.
    18. Zhang, Shuaibin & Du, Bowen & Ge, Mingwei & Zuo, Yingtao, 2022. "Study on the operation of small rooftop wind turbines and its effect on the wind environment in blocks," Renewable Energy, Elsevier, vol. 183(C), pages 708-718.
    19. He, Ruiyang & Yang, Hongxing & Sun, Haiying & Gao, Xiaoxia, 2021. "A novel three-dimensional wake model based on anisotropic Gaussian distribution for wind turbine wakes," Applied Energy, Elsevier, vol. 296(C).
    20. Li, Rui & Zhang, Jincheng & Zhao, Xiaowei, 2022. "Dynamic wind farm wake modeling based on a Bilateral Convolutional Neural Network and high-fidelity LES data," 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:eee:appene:v:303:y:2021:i:c:s0306261921010515. 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.