Summary of Reinforcement Learning to Maximise Wind Turbine Energy Generation, by Daniel Soler et al.
Reinforcement learning to maximise wind turbine energy generation
by Daniel Soler, Oscar Mariño, David Huergo, Martín de Frutos, Esteban Ferrer
First submitted to arxiv on: 17 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Mathematical Physics (math-ph); Optimization and Control (math.OC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed reinforcement learning strategy aims to optimize wind turbine energy generation by actively controlling rotor speed, yaw angle, and blade pitch in response to changing wind conditions. A double deep Q-learning agent with prioritized experience replay is trained using a blade element momentum model, enabling control for steady and dynamic winds. The agent outperforms classic PID control and value iteration reinforcement learning in all environments, showcasing adaptability to turbulent/gusty winds. Annual energy production calculations demonstrate the double deep Q-learning algorithm’s superiority over traditional methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Wind turbine energy generation can be optimized by using a special kind of artificial intelligence called reinforcement learning. This AI is trained to make decisions about controlling wind turbines based on changing weather conditions. The AI tries different ways to control the wind turbine, like adjusting the speed or direction of the blades, and sees what works best. The AI learns to adapt to changing weather conditions, like gusty winds, and produces more energy than traditional methods. This new approach is tested with real data and shows it can produce even more energy over a year. |
Keywords
* Artificial intelligence * Reinforcement learning
