A novel wake model for yawed wind turbines

Lopez, Daniel; Kuo, Jim; Li, Ni

doi:10.1016/j.energy.2019.04.120

Cited by 32 publications

(15 citation statements)

References 25 publications

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“…These models are formulated analytically and are very fast to evaluate, which makes them suitable for wind farm layout optimization. The further development of analytical models is still an active area, such as taking into account the turbulence effect [12,13], modelling yaw effects [14,15,16], modelling background flow effects [17], considering the expansion of physical wake boundary [18], and incorporating uncertainty based on high-fidelity data [19]. However, as these models are static, they are mainly used in turbine layout design for optimizing static quantities, such as mean power generation.…”

Section: Introductionmentioning

confidence: 99%

A novel dynamic wind farm wake model based on deep learning

Zhang

Zhao

2020

Applied Energy

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Section: Introductionmentioning

confidence: 99%

A novel dynamic wind farm wake model based on deep learning

Zhang

Zhao

2020

Applied Energy

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“…Qian and Ishihara [35] developed a mechanistic analytical wake model accounting for turbulence intensity, and validated with experimental results. Then, Lopez et al [38] developed an accurate and low-cost numerical field model, building on the unyawed wake model by Kuo et al [39] and the yawed wake model by Qian and Ishihara [35]. Shapiro et al [40] derived a new model based on the aerodynamic lifting line theory.…”

Section: Introductionmentioning

confidence: 99%

Wind Farm Yaw Optimization via Random Search Algorithm

Kuo

Pan

et al. 2020

Energies

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One direction in optimizing wind farm production is reducing wake interactions from upstream turbines. This can be done by optimizing turbine layout as well as optimizing turbine yaw and pitch angles. In particular, wake steering by optimizing yaw angles of wind turbines in farms has received significant attention in recent years. One of the challenges in yaw optimization is developing fast optimization algorithms which can find good solutions in real-time. In this work, we developed a random search algorithm to optimize yaw angles. Optimization was performed on a layout of 39 turbines in a 2 km by 2 km domain. Algorithm specific parameters were tuned for highest solution quality and lowest computational cost. Testing showed that this algorithm can find near-optimal (<1% of best known solutions) solutions consistently over multiple runs, and that quality solutions can be found under 200 iterations. Empirical results show that as wind farm density increases, the potential for yaw optimization increases significantly, and that quality solutions are likely to be plentiful and not unique.

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“…While mean streamwise velocity is important to predict wind energy and to optimize the wind farm, turbulence intensity is a critical parameter for determining the fatigue loads of wind turbines [55][56][57] and the key parameter for modeling wake recovery. 34,58,59 Thus, it is meaningful to provide information about turbulence intensities.…”

Section: Instantaneous Vortical Structures (ω Y )mentioning

confidence: 99%

Large eddy simulations of wind turbine wakes in typical complex topographies

Liu

Ishihara

2020

Wind Energy

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There is a need to clarify the coupling characteristics of terrain-induced wind fields and wind turbine-induced wake in wind farm micrositing. However, research investigating the effect of hill shape on this interaction is lacking. In addition, during the optimization of the layout of the turbines over topographies, the flow behind the turbines should be predicted in a fast way. After obtaining the wake flow of the turbine mounted on flat terrain and the flow over the topographies, there are mainly two superposition methods to predict the turbine wake flow over the topographies.One is to add the wind deficit following the location with a vertical distance of the hub height (D-line). The other is to add the wind deficit following the streamline starting from the turbine center (B-line). It remains unknown to what extent these two superposition methods can be adopted. Therefore, the effects of hill shape, wind turbine size, and turbine location are investigated. When the wind deficit, obtained from modeling the wake flow of the turbine mounted on flat terrain, is superposed following B-line, the results are overall better than those when following D-line.

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A novel wake model for yawed wind turbines

Cited by 32 publications

References 25 publications

A novel dynamic wind farm wake model based on deep learning

A novel dynamic wind farm wake model based on deep learning

Wind Farm Yaw Optimization via Random Search Algorithm

Large eddy simulations of wind turbine wakes in typical complex topographies

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