A Data-driven Yaw Coordination Control Method for Onshore Wind Farms based on the GCH Wake Model

Yanmei Shi, Xiongfei Liu, Fubao Li, Shengping Long, Wenqiang Du, Tong Yin

Abstract


To address the decreased power generation efficiency caused by wake effect in onshore wind farms, a yaw coordination control method based on the GCH (Gaussian-Curl-Hybrid) wake model is proposed. The study uses actual SCADA operation data to finely correct the incoming wind speed and power curves, thereby enhancing the model's applicability in complex terrains and high-turbulence environments. In addition, the SR (Serial-Refine) sequential optimization algorithm is introduced in the FLORIS (Flow Redirection and Induction in Steady-state) framework, and an efficient solution is achieved through a two-stage strategy of "global rough search - local fine adjustment". Taking 20 2.5MW wind turbines on a certain onshore wind farm as the research object, the optimization of yaw control for the WT01-WT03 series of wind turbines under different wind speeds and directions is analyzed. At a typical wind direction of 315 °, the optimal yaw angle of the upstream wind turbines increases with wind speed, and the gain efficiency shows a non-linear attenuation; under wind directions of 135° and 315°, the maximum power increases by 18.28% and 9.39% respectively, verifying the effectiveness and robustness of this method. The results provide theoretical support and engineering practice paths for intelligent operation control of onshore wind farms.

Keywords


GCH wake model; SCADA data processing; SR optimization algorithm; Wake effect; Yaw coordination control

Full Text:

PDF

References


Global Wind Energy Council, 2024. Global Wind Report 2024. Brussels: GWEC

Zong, H.Y., & Sun, E.B., 2022. Review of active wake control for horizontal-axis wind turbines. Acta Aerodynamica Sinica, 4(4): 51-68. http://dx.doi.org/10.7638/kqdlxxb-2021.0249.

Du, C.K., Zhu, Y.T., Zhu, L., Liu, Y., Gao, X.X., Deng, T., Yang, L.R., & Jia, Q.T., 2026. Research on energy efficiency enhancement control methods for wind turbines based on yaw wake models (In Chinese). China Measurement & Test, 52(2): 112-120. https://doi.org/10.11857/j.issn.1674-5124.2025010003.

Li, X.W., Xu, J.H., Zhu, R.Z., & Li, G.D., 2022. Study on power collaborative optimization of wind farm based on yaw wake model. Acta Energiae Solaris Sinica, 43(10): 144-151. https://doi.org/10.19912/j.0254-0096.tynxb.2021-1292.

Zhang, Z.L., Guo, N.Z., Yi, K., Wen, R.Q., & Shi, K.Z., 2024. Coordinated control of wind farm based on steady yaw. Acta Energiae Solaris Sinica, 45(6): 530-535. https://doi.org/10.19912/j.0254-0096.tynxb.2023-0207.

Jensen, N.O., 1983. A note on wind generator interaction. Risø-M-2411. Roskilde: Risø National Laboratory, 12. ISBN: 87-550-0971-9.

Bastankhah, M., & Porté-Agel, F., 2014. A new analytical model for wind turbine wakes. Renewable Energy, 70: 116-123. https://doi.org/10.1016/j.renene.2014.01.002.

Bastankhah, M., & Porté-Agel, F., 2016. Experimental and theoretical study of wind turbine wakes in yawed conditions. Journal of Fluid Mechanics, 806: 506-541. https://doi.org/10.1017/jfm.2016.595.

Zhu, X., Chen, Y., Xu, S., Zhang, S., Gao, X., Sun, H., & Lv, T., 2023. Three-dimensional non-uniform full wake characteristics for yawed wind turbine with LiDAR-based experimental verification. Energy, 270: 126907. https://doi.org/10.1016/j.energy.2023.126907.

King, J., Fleming, P., King, R., Martínez-Tossas, L. A., Bay, C. J., Mudafort, R., & Simley, E., 2021. Control-oriented model for secondary effects of wake steering. Wind Energy Science, 6(3): 701-714. https://doi.org/10.5194/wes-6-701-2021.

Martínez-Tossas, L. A., Annoni, J., Fleming, P. A., & Churchfield, M. J., 2019. The aerodynamics of the curled wake: a simplified model in view of flow control. Wind Energy Science, 4(1): 127-138. https://doi.org/10.5194/wes-4-127-2019.

Liu, Y.Y., Xin, Y.L., Tang, W.H., & Bourguet, S., 2021. Wake effect evaluation and yawing optimization in offshore wind farms based on Gaussian model (In Chinese). Guangdong Electric Power, 34(5): 1-10. https://doi.org/10.3969/j.issn.1007-290X.2021.005.001.

Huang, C., 2023. Optimal power generation control of wind farms with wake effect (In Chinese). Master’s thesis, TM614. Shanghai: Donghua University, China.

Fleming, P.A., Stanley, A.P., Bay, C J., King, J., Simley, E., Doekemeijer, B.M., & Mudafort, R., 2022. Serial-Refine Method for Fast Wake-Steering Yaw Optimization. Journal of Physics: Conference Series, 2265(3): 032109. https://doi.org/10.1088/1742-6596/2265/3/032109.

Niayifar, A., & Porté-Agel, F., 2016. Analytical modeling of wind farms: a new approach for power prediction. Energies, 9(9): 741. https://doi.org/10.3390/en9090741.

Crespo, A., & Herna, J., 1996. Turbulence characteristics in wind-turbine wakes. Journal of Wind Engineering and Industrial Aerodynamics, 61(1): 71-85. https://doi.org/10.1016/0167-6105(95)00033-X.

Heck, K.S., Johlas, H.M., & Howland, M.F., 2023. Modelling the induction, thrust and power of a yaw-misaligned actuator disk. Journal of Fluid Mechanics, 959: A9. https://doi.org/10.1017/jfm.2023.129.

Bodini, N., Lundquist, J.K., & Kirincich, A., 2020. Offshore wind turbines will encounter very low atmospheric turbulence. Journal of Physics: Conference Series, 1452(1): 012023. https://doi.org/10.1088/1742-6596/1452/1/012023.

Shid-Moosavi, S., Di Cioccio, F., Haghi, R., Tronci, E.M., Moaveni, B., Liberatore, S., & Hines, E., 2025. Modeling and experimentally-driven sensitivity analysis of wake-induced power loss in offshore wind farms: Insights from Block Island Wind Farm. Renewable Energy, 241: 122126. https://doi.org/10.1016/j.renene.2024.122126.

Howland, M.F., Lele, S.K., & Dabiri, J.O., 2019. Wind farm power optimization through wake steering. Proceedings of the National Academy of Sciences, 116(29): 14495-14500. https://doi.org/10.1073/pnas.1903680116.

Van Der Hoek, D., Doekemeijer, B.M., & van Wingerden, J.W., 2020. Predicting the benefit of wake steering on the annual energy production of a wind farm using large eddy simulations and Gaussian process regression. Journal of Physics: Conference Series, 1618(2): 022039. https://doi.org/10.1088/1742-6596/1618/2/022039.

Gori, F., Laizet, S., & Wynn, A., 2023. Sensitivity analysis of wake steering optimisation for wind farm power maximisation. Wind Energy Science, 8(9): 1425-1451. https://doi.org/10.5194/wes-8-1425-2023.




DOI: https://doi.org/10.64289/iej.26.0206.2484214