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

Research output: Contribution to conferencePaper

Abstract

In this paper, we present a reinforcement-learning based distributed approach to wind farm energy capture maximization using yaw control, also known as wake steering. In order to maximize the power output of a wind farm, it is often necessary for individual turbines to decrease their own power output through yaw misalignment so as 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 factors that are difficult to model or changing conditions. We propose an algorithm that adapts concepts of temporal difference reinforcement learning distributed to a multi-agent environment that allows individual turbines to act so as to optimize overall wind farm output and react to unforeseen disturbances.
Original languageAmerican English
Number of pages9
StatePublished - 2020
Event2020 American Control Conference (ACC) -
Duration: 1 Jul 20203 Jul 2020

Conference

Conference2020 American Control Conference (ACC)
Period1/07/203/07/20

Bibliographical note

See NREL/CP-5000-77744 for paper as published in proceedings

NREL Publication Number

  • NREL/CP-5000-75889

Keywords

  • controls
  • reinforcement learning
  • wind farm controls

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