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Model-free control of wind farms: A comparative study between individual and coordinated extremum seeking

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  • Ciri, Umberto
  • Rotea, Mario A.
  • Leonardi, Stefano

Abstract

Large Eddy Simulations of the turbulent flow over an array of wind turbines have been performed to evaluate a model-free approach to power optimization. Two different implementations have been tested: (i) individual extremum-seeking control (IESC), which optimizes the power of the single turbines individually; (ii) nested ESC (NESC), which coordinates the single controllers to seek a farm-level optimum. Both schemes provide a gain over the baseline, which operates all the turbines with ideal design set-points. These settings are found to be sub-optimal for waked turbines. The NESC provides a slightly larger power production than the independent ESC, albeit it has a slower convergence to the optimum. Therefore, depending on wind variability, both strategies may be employed. IESC is more appropriate for sites with wind conditions changing on a short time scale, while NESC should be preferred when the wind conditions are quite stable. Since the extremum-seeking algorithm is model-free, uncertainties in atmospheric conditions, aging of the turbine or numerical dissipation due to the sub-grid model should not change the general conclusions reached in this paper. This methodology can provide reliable results and permits to gain, through the analysis, a useful knowledge on the mechanisms leading to the performance enhancement.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:renene:v:113:y:2017:i:c:p:1033-1045
    DOI: 10.1016/j.renene.2017.06.065
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    Citations

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    Cited by:

    1. Karabacak, Murat, 2019. "A new perturb and observe based higher order sliding mode MPPT control of wind turbines eliminating the rotor inertial effect," Renewable Energy, Elsevier, vol. 133(C), pages 807-827.
    2. 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.
    3. Gionfra, Nicolò & Sandou, Guillaume & Siguerdidjane, Houria & Faille, Damien & Loevenbruck, Philippe, 2019. "Wind farm distributed PSO-based control for constrained power generation maximization," Renewable Energy, Elsevier, vol. 133(C), pages 103-117.
    4. 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.
    5. 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).
    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. 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.
    8. 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.
    9. 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.
    10. Felix Dietrich & Steffen Borchers-Tigasson & Till Naumann & Horst Schulte, 2021. "Adaptive Extremum Seeking Control of Urban Area Wind Turbines," Energies, MDPI, vol. 14(5), pages 1-12, March.

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