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Closed-loop model-based wind farm control using FLORIS under time-varying inflow conditions

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  • Doekemeijer, Bart M.
  • van der Hoek, Daan
  • van Wingerden, Jan-Willem

Abstract

Wind farm (WF) controllers adjust the control settings of individual turbines to enhance the total performance of a wind farm. Most WF controllers proposed in the literature assume a time-invariant inflow, whereas important quantities such as the wind direction and speed continuously change over time in reality. Furthermore, properties of the inflow are often assumed known, which is a fundamentally compromising assumption to make. This paper presents a novel, closed-loop WF controller that continuously estimates the inflow and maximizes the energy yield of the farm through yaw-based wake steering. The controller is tested in a high-fidelity simulation of a 6-turbine wind farm. The WF controller is stress-tested by subjecting it to strongly-time-varying inflow conditions over 5000 s of simulation. A time-averaged improvement in energy yield of 1.4% is achieved compared to a baseline, greedy controller. Moreover, the instantaneous energy gain is up to 11% for wake-loss-heavy situations. Note that this is the first closed-loop and model-based WF controller tested for time-varying inflow conditions (i.e., where the mean wind direction and wind speed change over time) at such fidelity. This solidifies the WF controller as the first realistic closed-loop control solution for yaw-based wake steering.

Suggested Citation

  • Doekemeijer, Bart M. & van der Hoek, Daan & van Wingerden, Jan-Willem, 2020. "Closed-loop model-based wind farm control using FLORIS under time-varying inflow conditions," Renewable Energy, Elsevier, vol. 156(C), pages 719-730.
    • Handle: RePEc:eee:renene:v:156:y:2020:i:c:p:719-730
    DOI: 10.1016/j.renene.2020.04.007
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    References listed on IDEAS

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    1. Boersma, S. & Doekemeijer, B.M. & Siniscalchi-Minna, S. & van Wingerden, J.W., 2019. "A constrained wind farm controller providing secondary frequency regulation: An LES study," Renewable Energy, Elsevier, vol. 134(C), pages 639-652.
    2. Kanev, Stoyan, 2020. "Dynamic wake steering and its impact on wind farm power production and yaw actuator duty," Renewable Energy, Elsevier, vol. 146(C), pages 9-15.
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    Cited by:

    1. Chanprasert, W. & Sharma, R.N. & Cater, J.E. & Norris, S.E., 2022. "Large Eddy Simulation of wind turbine fatigue loading and yaw dynamics induced by wake turbulence," Renewable Energy, Elsevier, vol. 190(C), pages 208-222.
    2. 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).
    3. Sun, Jili & Chen, Zheng & Yu, Hao & Gao, Shan & Wang, Bin & Ying, You & Sun, Yong & Qian, Peng & Zhang, Dahai & Si, Yulin, 2022. "Quantitative evaluation of yaw-misalignment and aerodynamic wake induced fatigue loads of offshore Wind turbines," Renewable Energy, Elsevier, vol. 199(C), pages 71-86.
    4. Sadek, Zein & Scott, Ryan & Hamilton, Nicholas & Cal, Raúl Bayoán, 2023. "A three-dimensional, analytical wind turbine wake model: Flow acceleration, empirical correlations, and continuity," Renewable Energy, Elsevier, vol. 209(C), pages 298-309.
    5. 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.
    6. 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).
    7. Maarten T. van Beek & Axelle Viré & Søren J. Andersen, 2021. "Sensitivity and Uncertainty of the FLORIS Model Applied on the Lillgrund Wind Farm," Energies, MDPI, vol. 14(5), pages 1-21, February.
    8. Kim, Taewan & Kim, Changwook & Song, Jeonghwan & You, Donghyun, 2024. "Optimal control of a wind farm in time-varying wind using deep reinforcement learning," Energy, Elsevier, vol. 303(C).
    9. Bart Matthijs Doekemeijer & Eric Simley & Paul Fleming, 2022. "Comparison of the Gaussian Wind Farm Model with Historical Data of Three Offshore Wind Farms," Energies, MDPI, vol. 15(6), pages 1-23, March.
    10. Tong Shu & Young Hoon Joo, 2023. "Non-Centralised Balance Dispatch Strategy in Waked Wind Farms through a Graph Sparsification Partitioning Approach," Energies, MDPI, vol. 16(20), pages 1-21, October.
    11. Zhiwen Deng & Chang Xu & Zhihong Huo & Xingxing Han & Feifei Xue, 2023. "Yaw Optimisation for Wind Farm Production Maximisation Based on a Dynamic Wake Model," Energies, MDPI, vol. 16(9), pages 1-20, May.
    12. Michael F. Howland & John O. Dabiri, 2020. "Influence of Wake Model Superposition and Secondary Steering on Model-Based Wake Steering Control with SCADA Data Assimilation," Energies, MDPI, vol. 14(1), pages 1-20, December.

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