Proof-of-Concept of a Reinforcement Learning Framework for Wind Farm Energy Capture Maximization in Time-Varying Wind

Research output: Contribution to journalArticlepeer-review

24 Scopus Citations

Abstract

In this paper, we present a proof-of-concept distributed reinforcement learning framework for wind farm energy capture maximization. The algorithm we propose uses Q-Learning in a wake-delayed wind farm environment and considers time-varying, though not yet fully turbulent, wind inflow conditions. These algorithm modifications are used to create the Gradient Approximation with Reinforcement Learning and Incremental Comparison (GARLIC) framework for optimizing wind farm energy capture in time-varying conditions, which is then compared to the FLOw Redirection and Induction in Steady State (FLORIS) static lookup table wind farm controller baseline.

Original languageAmerican English
Article numberArticle No. 043305
Number of pages14
JournalJournal of Renewable and Sustainable Energy
Volume13
Issue number4
DOIs
StatePublished - 1 Jul 2021

Bibliographical note

Publisher Copyright:
© 2021 Author(s).

NREL Publication Number

  • NREL/JA-5000-79690

Keywords

  • reinforcement learning
  • wind energy
  • wind plant controls
  • wind turbine controls

Fingerprint

Dive into the research topics of 'Proof-of-Concept of a Reinforcement Learning Framework for Wind Farm Energy Capture Maximization in Time-Varying Wind'. Together they form a unique fingerprint.

Cite this