Deep Reinforcement Learning for Active Wake Control

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

3 Citations (Scopus)
27 Downloads (Pure)


Wind farms suffer from so-called wake effects: when turbines are located in the wind shadows of other turbines, their power output is substantially reduced. These losses can be partially mitigated via actively changing the yaw from the individually optimal direction. Most existing wake control techniques have two major limitations: they use simplified wake models to optimize the control strategy, and they assume that the atmospheric conditions remain stable. In this paper, we address these limitations by applying reinforcement learning (RL). RL forgoes the wake model entirely and learns an optimal control strategy based on the observed atmospheric conditions and a reward signal, in this case the power output of the farm. It also accounts for random transitions in the observations, such as turbulent fluctuations in the wind. To evaluate RL for active wake control, we provide a simulator based on the state-of-the-art FLORIS model in the OpenAI gym format. Next, we propose three different state-action representations of the active wake control problem and investigate their effect on the performance of RL-based wake control. Finally, we compare RL to a state-of-the-art wake control strategy based on FLORIS and show that RL is less sensitive to changes in unobservable data.

Original languageEnglish
Title of host publicationInternational Conference on Autonomous Agents and Multiagent Systems, AAMAS 2022
PublisherInternational Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)
Number of pages10
ISBN (Electronic)9781713854333
Publication statusPublished - 2022
Event21st International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2022 - Auckland, Virtual, New Zealand
Duration: 9 May 202213 May 2022

Publication series

NameProceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS
ISSN (Print)1548-8403
ISSN (Electronic)1558-2914


Conference21st International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2022
Country/TerritoryNew Zealand
CityAuckland, Virtual

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.


  • Active Wake Control
  • Deep Reinforcement Learning
  • Wind Energy

Cite this