Proof-of-Concept of a Reinforcement Learning Framework for Wind Farm Energy Capture Maximization in Time-Varying Wind: Article No. 043305

Paul Stanfel, Kathryn Johnson, Christopher Bay, Jennifer King

Research output: Contribution to journalArticlepeer-review

22 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
Number of pages14
JournalJournal of Renewable and Sustainable Energy
Volume13
Issue number4
DOIs
StatePublished - 2021

NREL Publication Number

  • NREL/JA-5000-79690

Keywords

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

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