Reinforcement learning for wind-farm flow control: Current state and future actions

Mahdi Abkar*, Navid Zehtabiyan-Rezaie, Alexandros Iosifidis

*Corresponding author for this work

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperReviewResearchpeer-review

Abstract

Wind-farm flow control stands at the forefront of grand challenges in wind-energy science. The central issue is that current algorithms are based on simplified models and, thus, fall short of capturing the complex physics of wind farms associated with the high-dimensional nature of turbulence and multiscale wind-farm-atmosphere interactions. Reinforcement learning (RL), as a subset of machine learning, has demonstrated its effectiveness in solving high-dimensional problems in various domains, and the studies performed in the last decade prove that it can be exploited in the development of the next generation of algorithms for wind-farm flow control. This review has two main objectives. Firstly, it aims to provide an up-to-date overview of works focusing on the development of wind-farm flow control schemes utilizing RL methods. By examining the latest research in this area, the review seeks to offer a comprehensive understanding of the advancements made in wind-farm flow control through the application of RL techniques. Secondly, it aims to shed light on the obstacles that researchers face when implementing wind-farm flow control based on RL. By highlighting these challenges, the review aims to identify areas requiring further exploration and potential opportunities for future research.

Original languageEnglish
Article number100475
JournalTheoretical and Applied Mechanics Letters
Volume13
Issue6
Number of pages11
ISSN2095-0349
DOIs
Publication statusPublished - Nov 2023

Keywords

  • Machine learning
  • Power losses
  • Reinforcement learning
  • Turbine wakes
  • Wind-farm flow control

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