A Distributed Reinforcement Learning Yaw Control Approach for Wind Farm Energy Capture Maximization

Paul Stanfel, Kathryn Johnson, Christopher Bay, Jennifer King

Research output: Contribution to conferencePaperpeer-review

24 Scopus Citations

Abstract

In this paper, we present a reinforcement-learning-based distributed approach to wind farm energy capture maximization using yaw-based wake steering. In order to maximize the power output of a wind farm, individual turbines can use yaw misalignment to deflect their wakes away from downstream turbines. Although using model-based methods to achieve yaw misalignment is one option, a model-free method might be better suited to incorporate changing conditions and uncertainty. We propose an algorithm that adapts concepts of temporal difference reinforcement learning distributed to a multiagent environment that empowers individual turbines to optimize overall wind farm output and react to unforeseen disturbances.

Original languageAmerican English
Pages4065-4070
Number of pages6
DOIs
StatePublished - 2020
Event2020 American Control Conference, ACC 2020 - Denver, United States
Duration: 1 Jul 20203 Jul 2020

Conference

Conference2020 American Control Conference, ACC 2020
Country/TerritoryUnited States
CityDenver
Period1/07/203/07/20

Bibliographical note

See NREL/CP-5000-75889 for preprint

NREL Publication Number

  • NREL/CP-5000-77744

Keywords

  • artificial intelligence
  • energy capture
  • optimization
  • steady-state
  • wind farms
  • wind turbines

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