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Power and Flow Analysis of Axial Induction Control in an Array of Model-Scale Wind Turbines

Author

Listed:
  • Daniel Houck

    (Sandia National Laboratories, 1515 Eubank Blvd, SE, MS 1124, Albuquerque, NM 87123, USA
    These authors contributed equally to this work.)

  • Edwin A. Cowen

    (DeFrees Hydraulics Laboratory, School of Civil and Environmental Engineering, Cornell University, 220 Hollister Dr, Ithaca, NY 14853, USA
    These authors contributed equally to this work.)

Abstract

As research on wind energy has progressed, it has broadened from a focus on the wind turbine to include the entire wind farm. In particular, methods to mitigate the negative effects of upstream wakes on downstream turbines have received significant attention. One such mitigation method is axial induction control (AIC) in which upstream turbines are derated to reduce the momentum deficits in their wakes, leaving higher speed flow for downstream turbines. If performed correctly, it is theorized that the power production gains in downstream turbines can compensate for the power sacrificed by derating upstream turbines. Previous work has indicated that the “excess” energy left in the wake of the derated turbine is along the edges of the wake such that a turbine placed directly downstream will see little to no increase in power. To address this hypothesis, we performed a control and treatment experiment with model-scale turbines in a wide flume. Five turbines were arranged in three successive streamwise rows, with the first two rows consisting of two aligned turbines, while three turbines with small transverse spacing were placed in the third row, the central of which was also streamwise-aligned with the upstream two turbines. This arrangement was used to evaluate the difference in power production primarily among the turbines in the third row when the upstream turbines were derated. Particle image velocimetry (PIV) was used to measure the wake in the streamwise-vertical planes along the centerline of the array and along the rotor tips of the centerline turbines between all rows, and high accuracy power measurements were recorded from each turbine. The results show that the total power of the array was decreased while implementing AIC but that individual turbine performance differed from predictions. PIV results show that mean kinetic energy (MKE) is redistributed to the edges of the wakes as has been previously hypothesized. We provide an analysis of the results that connects both the power and flow measurements and that highlights several of the aspects of wind turbine wake flows that make them so complex and challenging to study.

Suggested Citation

  • Daniel Houck & Edwin A. Cowen, 2022. "Power and Flow Analysis of Axial Induction Control in an Array of Model-Scale Wind Turbines," Energies, MDPI, vol. 15(15), pages 1-27, July.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:15:p:5347-:d:869940
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    References listed on IDEAS

    as
    1. Sarlak, H. & Nishino, T. & Martínez-Tossas, L.A. & Meneveau, C. & Sørensen, J.N., 2016. "Assessment of blockage effects on the wake characteristics and power of wind turbines," Renewable Energy, Elsevier, vol. 93(C), pages 340-352.
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    4. Talavera, Miguel & Shu, Fangjun, 2017. "Experimental study of turbulence intensity influence on wind turbine performance and wake recovery in a low-speed wind tunnel," Renewable Energy, Elsevier, vol. 109(C), pages 363-371.
    5. Wim Munters & Johan Meyers, 2018. "Dynamic Strategies for Yaw and Induction Control of Wind Farms Based on Large-Eddy Simulation and Optimization," Energies, MDPI, vol. 11(1), pages 1-32, January.
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