mlsanityData to 14 Oct 2026

Reinforcement learning to maximise wind turbine energy generation

arXiv 2402.11384 ↗First posted 17 Feb 2024cs.LG math-ph math.MPcited by 0

On the map: Deep reinforcement learning methods → RL for energy, networks and operations

Abstract (arXiv)

We propose a reinforcement learning strategy to control wind turbine energy generation by actively changing the rotor speed, the rotor yaw angle and the blade pitch angle. A double deep Q-learning with a prioritized experience replay agent is coupled with a blade element momentum model and is trained to allow control for changing winds. The agent is trained to decide the best control (speed, yaw, pitch) for simple steady winds and is subsequently challenged with real dynamic turbulent winds, showing good performance. The double deep Q- learning is compared with a classic value iteration reinforcement learning control and both strategies outperform a classic PID control in all environments. Furthermore, the reinforcement learning approach is well suited to changing environments including turbulent/gusty winds, showing great adaptability. Finally, we compare all control strategies with real winds and compute the annual energy production. In this case, the double deep Q-learning algorithm also outperforms classic methodologies.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 7 of its 34 references that are in the map. First posted on arXiv 17 Feb 2024.

Builds on

7 of 34 references.

References · 7

  1. 2013Playing Atari with Deep Reinforcement Learningcited by 3,274
  2. 2019Optuna: A Next-generation Hyperparameter Optimization Frameworkcited by 2,501
  3. 2015Deep Reinforcement Learning with Double Q-learningcited by 1,745
  4. 2015Prioritized Experience Replaycited by 1,099
  5. 2019A review on Deep Reinforcement Learning for Fluid Mechanicscited by 66
  6. 2022Improving aircraft performance using machine learning: a reviewcited by 19
  7. 2023An Improved Yaw Control Algorithm for Wind Turbines via Reinforcement Learningcited by 3

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2024Deep Reinforcement Learning for Multi-Objective Optimization: Enhancing Wind Turbine Energy Generation while Mitigating Noise Emissions
  2. 2025Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control
  3. 2026Accelerating Reinforcement Learning for Wind Farm Control via Expert Demonstrations
  4. 2024Harvesting energy from turbulent winds with Reinforcement Learning
  5. 2025WFCRL: A Multi-Agent Reinforcement Learning Benchmark for Wind Farm Control
  6. 2025How to craft a deep reinforcement learning policy for wind farm flow control
  7. 2026Offline Reinforcement Learning for Wind Farm Control: A Wind Tunnel Study under Dynamic Wind Directions
  8. 2026Control of hybrid wind-wave energy systems using reinforcement learning ↗