A Framework for Autonomous Wind Farms: Wind Direction Consensus

Kathryn Johnson, Eliot Quon, Paul Fleming, Christopher Bay, Emiliano Dall'Anese, Travis Kemper

Research output: Contribution to journalArticle


Wind turbines in a wind farm typically operate individually to maximize their own performance and do not take into account information from nearby turbines. In an autonomous wind farm, enabling cooperation to achieve farm-level objectives, turbines will need to use information from nearby turbines to optimize performance, ensure resiliency when other sensors fail, and adapt to changing local conditions. A key element of achieving an autonomous wind farm is to develop algorithms that provide necessary information to ensure reliable, robust, and efficient operation of wind turbines in a wind plant using local sensor information that is already being collected, such as supervisory control and data acquistion (SCADA) data, local meteorological stations, and nearby radars/sodars/lidars. This article presents a framework for implementing an autonomous wind farm that incorporates information from local sensors in real time to better align turbines in a wind farm. Oftentimes, measurements made at an individual turbine are noisy and unreliable. By incorporating measurements from multiple nearby turbines, a more robust estimate of the wind direction can be obtained at an individual turbine. Results indicate that this estimate of the wind direction can be used to improve the turbine's knowledge of the wind direction and could decrease dynamic yaw misalignment, decrease the amount of time a turbine spends yawing due to a more robust input to the yaw controller, and increase resiliency to faulty wind-vane measurements.
Original languageAmerican English
Number of pages17
JournalWind Energy Science Discussions
StatePublished - 2018

Bibliographical note

See NREL/JA-5000-74366 for final paper as published in Wind Energy Science

NREL Publication Number

  • NREL/JA-5000-72232


  • distributed optimization
  • wind farm control


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