Wind Farm Collective Yaw Control

Stage: Development
Analysis by the U.S. Energy Information Administration (EIA) indicates that wind’s annual share of electricity generation will exceed hydropower’s share for the first time in 2019, making it the largest renewable energy producer in the U.S. With such expansive growth, new innovations are continually needed to optimize wind performance and reduce its levelized cost of energy (LCOE).


Wind researchers at NREL are at the forefront of such efforts and have recently taken a novel approach to optimally govern the directional orientations of turbines in a wind farm.

A wind turbine’s orientation is typically controlled by an independent wind direction measurement device mounted on the nacelle – the protective housing of the turbine’s power generator. These yaw-controlling devices generally observe wind direction continuously while in a fixed wind direction, then only move the turbine to a new location when a persistent or large enough wind change happens. Significant issues from this independent form of control are slow response time to definitive changes in wind direction and potential loss of optimal energy output. Also, a turbine can falsely react to micro wind events that can inefficiently and counterproductively point it against prevailing wind patterns, thereby requiring it to revert to its original direction yet again.

To overcome such issues, NREL researchers have developed an innovative, autonomous collective wind farm approach whereby wind direction measurements from individual turbines and SCADA sensors are collected throughout a wind farm and contribute to an overarching, real-time consensus of wind direction. This consensus information is then used to control the yaw directions of each of the turbines in the farm. Such a method optimizes the positioning and energy capture of all the turbines and allows for ideal orientation ahead of anticipated wind changes as they sweep through the kilometers-wide wind farms. Furthermore, the network connections in the framework can be based on proximity of one wind turbine to another, and thus provide optimized corrections for aerodynamic interactions (wakes) between the turbines, for localized landscape variations, and for other specified performance metrics required by the wind farm’s developer/user.

To learn more about this autonomous, collective wind farm control technology, please contact Erin Beaumont at:

Erin.Beaumont@nrel.gov

ROI 18-50

Applications and Industries

  • Wind Generation
  • Benefits

  • Increased power production from improved yaw alignment
  • Detection of individual sensor failures through consensus comparisons
  • Load reductions through decreases in periods of misalignment.
  • Avoidance of wear-and-tear on yaw systems through expected decreases in un-needed yaw motions.