Tootfinder

Modeling wind farm noise emission and propagation: effects of flow layout
J. Colas, A. Emmanuelli, D. Dragna, R. J. A. M. Stevens
https://arxiv.org/abs/2508.13128 https://

Modeling wind farm noise emission and propagation: effects of flow layout
This study demonstrates how wind farm flow physics influence noise generation and downstream propagation through numerical simulations. The flow field is modeled using large-eddy simulations (LES), and the time-averaged output serves as input to acoustic models that predict wind turbine noise. In the first turbine row, turbulence-induced noise (TIN) and trailing edge noise (TEN) contribute equally, with TIN dominating at low frequencies and TEN at higher frequencies. Downstream, TEN decreases d…

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…

Probabilistic Forecasting Method for Offshore Wind Farm Cluster under Typhoon Conditions: a Score-Based Conditional Diffusion Model
Jinhua He, Zechun Hu
https://arxiv.org/abs/2508.10705

Probabilistic Forecasting Method for Offshore Wind Farm Cluster under Typhoon Conditions: a Score-Based Conditional Diffusion Model
Offshore wind power (OWP) exhibits significant fluctuations under typhoon conditions, posing substantial challenges to the secure operation of power systems. Accurate forecasting of OWP is therefore essential. However, the inherent scarcity of historical typhoon data and stochasticity of OWP render traditional point forecasting methods particularly difficult and inadequate. To address this challenge and provide grid operators with the comprehensive information necessary for decision-making, thi…

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…

Flying Base Stations for Offshore Wind Farm Monitoring and Control: Holistic Performance Evaluation and Optimization
Xinyi Lin, Peizheng Li, Adnan Aijaz
https://arxiv.org/abs/2507.07832

Flying Base Stations for Offshore Wind Farm Monitoring and Control: Holistic Performance Evaluation and Optimization
Ensuring reliable and low-latency communication in offshore wind farms is critical for efficient monitoring and control, yet remains challenging due to the harsh environment and lack of infrastructure. This paper investigates a flying base station (FBS) approach for wide-area monitoring and control in the UK Hornsea offshore wind farm project. By leveraging mobile, flexible FBS platforms in the remote and harsh offshore environment, the proposed system offers real-time connectivity for turbines…

Wind theft. http://archive.today/2025.04.04-220801/https://www.newscientist.com/article/2475172-wind-farm-developers-are-worried-about-neighbours-stealing-their-wind/

"The Berwick Bank wind farm, located off the eastern coast of Scotland could provide power to 6 million homes."
Scotland Gives Go-Ahead for World’s Largest Offshore Wind Farm - Bloomberg
https://archive.ph/dTs18#selection-1527.0-1531.94

Die britische Regierung hebt den maximalen Vergütungspreis für neue #Windenergie-Projekte an, um Investitionen in #ErneuerbareEnergien zu fördern und das Ziel eines nahezu fossilfreien Stromnetzes bis 2030 zu erreichen.
Offshore-Wind darf künftig bis zu £113/MWh kosten, Floatin…

Government raises maximum guaranteed price for wind energy
The government increases the maximum price it will guarantee wind farm developers for electricity.

Power loss mechanisms and optimal induction factors for realistic large wind farms
Takafumi Nishino, Amanda S. M. Smyth
https://arxiv.org/abs/2508.02727 https://

Power loss mechanisms and optimal induction factors for realistic large wind farms
Power loss mechanisms in large wind farms are complex due to the multiscale nature of wind farm aerodynamics. Recent studies based on the two-scale momentum theory have brought new insights into this field; however, most of them have been limited to idealised wind farm scenarios. To better understand the power performance of real wind farms, in this study we extend the framework of the two-scale momentum theory to non-ideal turbine design and layout scenarios, and then introduce simple analytic…

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…

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…