Wake Meandering

Larsen, Gunner Chr.

doi:10.1007/978-3-030-05455-7_50-1

Cited by 3 publications

(4 citation statements)

References 43 publications

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“…51 Bastankhah and Porté-Agel proposed a single-Gaussian distribution to approximate the wake velocity in far wake, 52 and it was later improved using a double-Gaussian distribution. 53,54 The medium-fidelity dynamic wake meandering model 55 and Fuga wake model 56 numerically solve simplified RANS equations to evaluate wake deficit, turbulence, and meandering. Another branch of medium-fidelity wake models includes some variations of the actuator disk model.…”

Section: Wake Modelsmentioning

confidence: 99%

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Optimal selection of time windows for preventive maintenance of offshore wind farms subject to wake losses

Zhang,

Chowdhury,

Zhang

et al. 2023

Wind Energy

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The maintenance of wind farms is one of the major factors affecting their profitability. During preventive maintenance, the shutdown of wind turbines causes downtime energy losses. The selection of when and which turbines to maintain can significantly impact the overall downtime energy loss. This paper leverages a wind farm power generation model to calculate downtime energy losses during preventive maintenance for an offshore wind farm. Wake effects are considered to accurately evaluate power output under specific wind conditions. In addition to wind speed and direction, the influence of wake effects is an important factor in selecting time windows for maintenance. To minimize the overall downtime energy loss of an offshore wind farm caused by preventive maintenance, a mixed‐integer nonlinear optimization problem is formulated and solved by the genetic algorithm, which can select the optimal maintenance time windows of each turbine. Weather conditions are imposed as constraints to ensure the safety of maintenance personnel and transportation. Using the climatic data of Cape Cod, Massachusetts, the schedule of preventive maintenance is optimized for a simulated utility‐scale offshore wind farm. The optimized schedule not only reduces the annual downtime energy loss by selecting the maintenance dates when wind speed is low but also decreases the overall influence of wake effects within the farm. The portion of downtime energy loss reduced due to consideration of wake effects each year is up to approximately 0.2% of the annual wind farm energy generation across the case studies—with other stated opportunities for further profitability improvements.

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Section: Wake Modelsmentioning

confidence: 99%

“…The medium‐fidelity dynamic wake meandering model 55 and Fuga wake model 56 numerically solve simplified RANS equations to evaluate wake deficit, turbulence, and meandering. Another branch of medium‐fidelity wake models includes some variations of the actuator disk model 57,58 …”

Section: Introductionmentioning

confidence: 99%

Optimal selection of time windows for preventive maintenance of offshore wind farms subject to wake losses

Zhang,

Chowdhury,

Zhang

et al. 2023

Wind Energy

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“…Therefore, in the RANS approach, there are branches of models using different assumptions and simplifications to relax these terms in order to closure the turbulence equations. These turbulence models based on the empirical observations are widely developed, which include adopting Prandtl's mixing length theory [65], eddy-viscosity closure [66], [67], k − ϵ closure [68], and dynamic wake meandering model [69], [70]. The RANS approach substantially reduces the computational cost, and it has been widely applied in the industrial CFD applications for the past decades [71].…”

Section: -2-2 Ransmentioning

confidence: 99%

Enhancing Wake Mixing in Wind Farms by Multi-Sine Signals in the Helix Approach

Huang,

Mulders,

Taschner

et al. 2023

2023 American Control Conference (ACC)

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“…Some examples of high fidelity simulators are SOWFA 23 , PALM 24 and SP-Wind 25 . Finally, medium fidelity models (such as DWM 26 , FAST.Farm 27 , WakeFarm 28 ) are based on simplifications of the Navier-Stokes equations. Despite, the simplifications, the computing time for medium fidelity models remains significant (order of minutes on PC) 4 .…”

Section: Introductionmentioning

confidence: 99%

Modular deep learning approach for wind farm power forecasting and wake loss prediction

Ally,

Daems,

Verstraeten

et al. 2023

Preprint

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Power production of offshore wind farms depends on many parameters and is significantly affected by wake losses. Due to the intermittency of wind power and its rapidly increasing share in the total energy mix, accurate forecasting of wind farm power production becomes increasingly important. This paper presents a data-driven methodology for forecasting power production and wake losses of wind farms, taking the dynamics of weather conditions into account. A modular approach is adopted by integrating multiple deep neural networks, resulting in a digital twin of the wind farm that can be interfaced with weather forecasts of different meteorological service providers. Another key advantage of the employed data-driven approach is its high prediction speed compared to physics-based methods, such that it can be employed for applications where real-time power forecasting is required. The methodology has been applied to two large offshore wind farms located within the Belgian-Dutch wind farm cluster in the North Sea.

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Wake Meandering

Cited by 3 publications

References 43 publications

Optimal selection of time windows for preventive maintenance of offshore wind farms subject to wake losses

Optimal selection of time windows for preventive maintenance of offshore wind farms subject to wake losses

Enhancing Wake Mixing in Wind Farms by Multi-Sine Signals in the Helix Approach

Modular deep learning approach for wind farm power forecasting and wake loss prediction

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