Wind Direction Estimation Using SCADA Data with Consensus-Based Optimization

Jennifer Annoni, Christopher Bay, Kathryn Johnson, Emiliano Dall’Anese, Eliot Quon, Travis Kemper, Paul Fleming

Research output: Contribution to journalArticlepeer-review

34 Scopus Citations


Wind turbines in a wind farm typically operate individually to maximize their own performance and do not take into account information from nearby turbines. To enable 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 a more efficient wind farm is to develop algorithms that ensure reliable, robust, real-time, and efficient operation of wind turbines in a wind farm using local sensor information that is already being collected, such as supervisory control and data acquisition (SCADA) data, local meteorological stations, and nearby radars/sodars/lidars. This article presents a framework for developing a cooperative wind farm that incorporates information from nearby turbines in real time to better align turbines in a wind farm. SCADA data from multiple turbines can be used to make better estimates of the local inflow conditions at each individual turbine. By incorporating measurements from multiple nearby turbines, a more reliable estimate of the wind direction can be obtained at an individual turbine. The consensus-based approach presented in this paper uses information from nearby turbines to estimate wind direction in an iterative way rather than aggregating all the data in a wind farm at once. Results indicate that this estimate of the wind direction can be used to improve the turbine’s knowledge of the wind direction. This estimated wind direction signal has implications for potentially decreasing dynamic yaw misalignment, decreasing the amount of time a turbine spends yawing due to a more reliable input to the yaw controller, increasing resiliency to faulty wind-vane measurements, and increasing the potential for wind farm control strategies such as wake steering.

Original languageAmerican English
Pages (from-to)355-368
Number of pages14
JournalWind Energy Science
Issue number2
StatePublished - 2019

Bibliographical note

See NREL/JA-5000-72232 for paper as published in Wind Energy Science Discussions

NREL Publication Number

  • NREL/JA-5000-74366


  • distributed optimization
  • wind farm control


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