Data-Driven Wind Farm Optimization Incorporating Effects of Turbulence Intensity

Ryan King, Christiane Adcock

Research output: Contribution to conferencePaperpeer-review

10 Scopus Citations


We demonstrate assimilation of field data from a nacelle-mounted lidar and meteorological tower into a medium-fidelity Reynolds-Averaged Navier Stokes wind farm flow model to better predict the effects of atmospheric stability. Increased predictability under a variety of atmospheric conditions can lead to more effective control design and optimization of a wind farm. In particular, atmospheric stability affects wind turbine wake propagation and, therefore, aspects of wind farm control and performance, such as active wind farm control, layout optimization, and power output. Accurately modeling wakes in different stability conditions remains a persistent challenge. This paper presents an optimization framework that leverages high-fidelity field or simulation data to correct a lower-fidelity flow model. Optimal model corrections are found by solving a regularized, high-dimensional, gradient-based optimization problem using adjoint flowfield information. We validate the trained model against large eddy simulation results, and perform separate gradient-based layout optimizations of a simulated utility-scale wind farm to maximize power. Using the data-driven model corrections, we find that atmospheric stability significantly impacts layout optimization and power production: the optimal layout for stable conditions produced 9.1% more power than the optimal layout for unstable conditions, and the optimal layout for neutral conditions underperformed by 8.5% in unstable conditions.

Original languageAmerican English
Number of pages6
StatePublished - 9 Aug 2018
Event2018 Annual American Control Conference, ACC 2018 - Milwauke, United States
Duration: 27 Jun 201829 Jun 2018


Conference2018 Annual American Control Conference, ACC 2018
Country/TerritoryUnited States

Bibliographical note

Publisher Copyright:
© 2018 AACC.

NREL Publication Number

  • NREL/CP-2C00-72479


  • atmospheric modeling
  • data models
  • laser radar
  • optimization
  • stability analysis
  • wind farms
  • wind turbines


Dive into the research topics of 'Data-Driven Wind Farm Optimization Incorporating Effects of Turbulence Intensity'. Together they form a unique fingerprint.

Cite this