Tootfinder

Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control
Andrew Mole, Max Weissenbacher, Georgios Rigas, Sylvain Laizet
https://arxiv.org/abs/2506.20554

Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control
Traditional wind farm control operates each turbine independently to maximize individual power output. However, coordinated wake steering across the entire farm can substantially increase the combined wind farm energy production. Although dynamic closed-loop control has proven effective in flow control applications, wind farm optimization has relied primarily on static, low-fidelity simulators that ignore critical turbulent flow dynamics. In this work, we present the first reinforcement learnin…

How to craft a deep reinforcement learning policy for wind farm flow control
Elie Kadoche, Pascal Bianchi, Florence Carton, Philippe Ciblat, Damien Ernst
https://arxiv.org/abs/2506.06204

How to craft a deep reinforcement learning policy for wind farm flow control
Within wind farms, wake effects between turbines can significantly reduce overall energy production. Wind farm flow control encompasses methods designed to mitigate these effects through coordinated turbine control. Wake steering, for example, consists in intentionally misaligning certain turbines with the wind to optimize airflow and increase power output. However, designing a robust wake steering controller remains challenging, and existing machine learning approaches are limited to quasi-sta…

Instantaneous Failure, Repair and Mobility Rates for Markov Reliability Systems: A Wind-Farm application
Guglielmo D'Amico, Filippo Petroni
https://arxiv.org/abs/2506.17280

Instantaneous Failure, Repair and Mobility Rates for Markov Reliability Systems: A Wind-Farm application
The Rate of Occurrence of Failures (ROCOF) is a widely utilized indicator for assessing a system's performance over time, yet it does not fully disclose the instantaneous behavior of a system. This paper introduces new measures to complement the ROCOF, providing a more comprehensive understanding of system reliability, particularly for Markov systems. We define the Rate of Occurrence of Repairs (ROCOR), which quantifies the system's instantaneous tendency to transition from failure to working s…

Symbolic Regression-Enhanced Dynamic Wake Meandering: Fast and Physically Consistent Wind-Turbine Wake Modeling
Ding Wang, Dachuan Feng, Kangcheng Zhou, Yuntian Chen, Shijun Liao, Shiyi Chen
https://arxiv.org/abs/2506.14403

Symbolic Regression-Enhanced Dynamic Wake Meandering: Fast and Physically Consistent Wind-Turbine Wake Modeling
Accurately modeling wind turbine wakes is essential for optimizing wind farm performance but remains a persistent challenge. While the dynamic wake meandering (DWM) model captures unsteady wake behavior, it suffers from near-wake inaccuracies due to empirical closures. We propose a Symbolic Regression-enhanced DWM (SRDWM) framework that achieves equation-level closure by embedding symbolic expressions for volumetric forcing and boundary terms explicitly into governing equations. These physicall…