Deep Reinforcement Learning for Automatic Generation Control of Wind Farms

Sanjana Vijayshankar, Paul Stanfel, Jennifer King, Evangelia Spyrou, Kathryn Johnson

Research output: Contribution to conferencePaperpeer-review

13 Scopus Citations


This paper provides a model-free framework for real-time control of wind farms to accurately track a power reference signal. This problem requires tractable dynamical models for capturing the aerodynamic interaction between wind turbines and controllers that can make decisions in realtime given varying atmospheric conditions. In this paper, we propose a deep reinforcement learning framework to provide real-time yaw control of a wind farm. Modifications have been made to FLOw Redirection and Induction in Steady State (FLORIS), a modeling tool that incorporates transient wake behavior. The control problem is formulated to track a synthetic power reference signal based on historical atmospheric (wind speed and direction) information, price signals, and regulation deployment data from U.S. regional transmission operators. Results indicate that a wind farm, with this control paradigm, can achieve good tracking performance when tested with real atmospheric data.

Original languageAmerican English
Number of pages7
StatePublished - 25 May 2021
Event2021 American Control Conference, ACC 2021 - Virtual, New Orleans, United States
Duration: 25 May 202128 May 2021


Conference2021 American Control Conference, ACC 2021
Country/TerritoryUnited States
CityVirtual, New Orleans

Bibliographical note

Publisher Copyright:
© 2021 American Automatic Control Council.

NREL Publication Number

  • NREL/CP-5000-79145


  • grid services
  • reinforcement learning
  • wind farm controls


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