Wake effect measurement in complex terrain - A case study in Brazilian wind farms

Böhme, Gustavo S.; Fadigas, Eliane Aparecida Faria Amaral; Gimenes, André Luiz Veiga; Tassinari, Carlos E.M.

doi:10.1016/j.energy.2018.07.119

Cited by 17 publications

(9 citation statements)

References 28 publications

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“…Alternatively, CFD solvers are based on the solution to the averaged or filtered Navier Stoke equations. In current years, numerous works [22][23][24] have presented models for simulating the turbine wakes and associated power losses of actual wind farms.…”

Section: Wake Modelsmentioning

confidence: 99%

Key Issues on the Design of an Offshore Wind Farm Layout and Its Equivalent Model

Zeng

2019

Applied Sciences

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Offshore wind farms will have larger capacities in the future than they do today. Thus, the costs that are associated with the installation of wind turbines and the connection of power grids will be much higher, thus the location of wind turbines and the design of internal cable connections will be even more important. A large wind farm comprises of hundreds of wind turbines. Modeling each using a complex model leads to long simulation times—especially in transient response analyses. Therefore, in the future, simulations of power systems with a high wind power penetration must apply the equivalent wind-farm model to reduce the burden of calculation. This investigation examines significant issues around the optimal design of a modern offshore wind farm layout and its equivalent model. According to a review of the literature, the wake effect and its modeling, layout optimization technologies, cable connection design, and wind farm reliability, are significant issues in offshore wind farm design. This investigation will summarize these important issues and present a list of factors that strongly influence the design of an offshore wind farm.

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Section: Wake Modelsmentioning

confidence: 99%

Key Issues on the Design of an Offshore Wind Farm Layout and Its Equivalent Model

Zeng

2019

Applied Sciences

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“…It is well noted that in most previous related studies, wind energy assessment depended primarily on the characteristics of wind speed, whereas the information regarding the effect of varying wind direction was somewhat limited 6–9 . For wind turbines located in low‐speed and complex terrain regions, such treatment may be sometimes misleading 10–12 . To cope with this problem, this study focuses on the joint probability density function (JPDF) of wind speed and wind direction, which are expected to provide valuable insights to enhance the quantification of direction‐related wind energy potential.…”

Section: Introductionmentioning

confidence: 99%

“…[6][7][8][9] For wind turbines located in low-speed and complex terrain regions, such treatment may be sometimes misleading. [10][11][12] To cope with this problem, this study focuses on the joint probability density function (JPDF) of wind speed and wind direction, which are expected to provide valuable insights to enhance the quantification of direction-related wind energy potential.…”

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confidence: 99%

Copula‐based joint distribution analysis of wind speed and wind direction: Wind energy development for Hong Kong

Huang

Shu

et al. 2023

Wind Energy

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Accurate and reliable assessment of wind energy potential has important implication to the wind energy industry. Most previous studies on wind energy assessment focused solely on wind speed, whereas the dependence of wind energy on wind direction was much less considered and documented. In this paper, a copula‐based method is proposed to better characterize the direction‐related wind energy potential at six typical sites in Hong Kong. The joint probability density function (JPDF) of wind speed and wind direction is constructed by a series of copula models. It shows that Frank copula has the best performance to fit the JPDF at hilltop and offshore sites while Gumbel copula outperforms other models at urban sites. The derived JPDFs are applied to estimate the direction‐related wind power density at the considered sites. The obtained maximum direction‐related wind energy density varies from 41.3 W/m2 at an urban site to 507.9 W/m2 at a hilltop site. These outcomes are expected to facilitate accurate micro‐site selection of wind turbines, thereby improving the economic benefits of wind farms in Hong Kong. Meanwhile, the developed copula‐based method provides useful references for further investigations regarding direction‐related wind energy assessments at various terrain regions. Notably, the proposed copula‐based method can also be applied to characterize the direction‐related wind energy potential somewhere other than Hong Kong.

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“…Thus the low-fidelity wake models based on analytical formulations [19,20,21] remain the main tool in the industry and are still under active development [22,23,24,25]. However, these models are not able to precisely predict the detailed flow dynamics and careful calibration of empirical model parameters is often needed to increase the prediction accuracy [26]. The underlying uncertainty of analytical wake models needs to be investigated in order to achieve reliable predictions of wind turbine wakes.…”

Section: Introductionmentioning

confidence: 99%

Quantification of parameter uncertainty in wind farm wake modeling

Zhang

Zhao

2020

Energy

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Wake effect measurement in complex terrain - A case study in Brazilian wind farms

Cited by 17 publications

References 28 publications

Key Issues on the Design of an Offshore Wind Farm Layout and Its Equivalent Model

Key Issues on the Design of an Offshore Wind Farm Layout and Its Equivalent Model

Copula‐based joint distribution analysis of wind speed and wind direction: Wind energy development for Hong Kong

Quantification of parameter uncertainty in wind farm wake modeling

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