Lidar measurements of yawed-wind-turbine wakes: characterization and validation of analytical models

Brugger, Peter; Debnath, Mithu; Scholbrock, Andrew; Fleming, Paul; Moriarty, Patrick; Simley, Eric; Jäger, D.; Roadman, Jason; Murphy, Mark S.; Zong, Haohua; Porté‐Agel, Fernando

doi:10.5194/wes-5-1253-2020

Cited by 30 publications

(29 citation statements)

References 34 publications

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“…Details of cross‐stream components of wake velocity have been examined by Martínez‐Tossas et al, 11 in which a ‘curled‐wake’ model is formulated, and by Shapiro et al, 12 in which yawed turbines are viewed as lifting lines. In recent developments, several wind tunnel studies (for example, 13‐16 ), numerical simulations (for example, 17‐20 ), as well as field studies (for example, 21‐26 ) have examined wake steering for wind farms of single‐rotor wind turbines, in which improvements have been seen in terms of both power output and reliability.…”

Section: Introductionmentioning

confidence: 99%

Wake steering of multirotor wind turbines

et al. 2021

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In this paper, wake steering is applied to multirotor turbines to determine whether it has the potential to reduce wind plant wake losses. Through application of rotor yaw to multirotor turbines, a new degree of freedom is introduced to wind farm control such that wakes can be expanded, channelled or redirected to improve inflow conditions for downstream turbines. Five different yaw configurations are investigated (including a baseline case) by employing large‐eddy simulations (LES) to generate a detailed representation of the velocity field downwind of a multirotor wind turbine. Two lower‐fidelity models from single‐rotor yaw studies (curled‐wake model and analytical Gaussian wake model) are extended to the multirotor case, and their results are compared with the LES data. For each model, the wake is analysed primarily by examining wake cross‐sections at different downwind distances. Further quantitative analysis is carried out through characterisations of wake centroids and widths over a range of streamwise locations and through a brief analysis of power production. Most significantly, it is shown that rotor yaw can have a considerable impact on both the distribution and magnitude of the wake velocity deficit, leading to power gains for downstream turbines. The lower‐fidelity models show small deviation from the LES results for specific configurations; however, both are able to reasonably capture the wake trends over a large streamwise range.

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Section: Introductionmentioning

confidence: 99%

Wake steering of multirotor wind turbines

et al. 2021

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“…The proposed model has shown an acceptable agreement with the results obtained by large eddy simulations (LES) [28], LiDAR measurements [24,29], and wind tunnel [28,30]. It presents a significant improvement with respect to the models commonly used in commercial packages such as WAsP or OpenWind.…”

Section: Wake Effect Modelmentioning

confidence: 52%

Optimal Pitch Angle Strategy for Energy Maximization in Offshore Wind Farms Considering Gaussian Wake Model

2021

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This paper presents a new approach based on the optimization of the blade pitching strategy of offshore wind turbines in order to maximize the global energy output considering the Gaussian wake model and including the effect of added turbulence. A genetic algorithm is proposed as an optimization tool in the process of finding the optimal setting of the wind turbines, which aims to determine the individual pitch of each turbine so that the overall losses due to the wake effect are minimised. The integration of the Gaussian model, including the added turbulence effect, for the evaluation of the wakes provides a step forward in the development of strategies for optimal operation of offshore wind farms, as it is one of the state-of-the-art analytical wake models that allow the evaluation of the energy output of the project in a more reliable way. The proposed methodology has been tested through the execution of a set of test cases that show the ability of the proposed tool to maximize the energy production of offshore wind farms, as well as highlights the importance of considering the effect of added turbulence in the evaluation of the wake.

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“…Our proposed ABL model can be used as an inflow model for three-dimensional wind farm simulations to isolate the effects of wind veer or ABL depth, when the results are compared with wind farm simulations using an inflow based on the ABL model of Apsley and Castro (1997) (with wind veer) or a neutral surface layer, respectively. Isolating the effect of wind veer can be of interest for wake steering control studies, where wind veer can have a significant impact as discussed by Brugger et al (2020). In addition, it is possible 2 https://doi.org/10.5194/wes-2020-130 Preprint.…”

Section: Introductionmentioning

confidence: 99%

A pressure-driven atmospheric boundary layer model satisfying Rossby- and Reynolds number similarity

Laan¹,

Kelly²,

Baungaard³

2021

Preprint

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Abstract. Idealized models of the atmospheric boundary layer (ABL) can be used to leverage understanding of the interaction between the ABL and wind farms, towards improvement of wind farm flow modelling. We propose a pressure-driven one-dimensional ABL model without wind veer, which can be used as an inflow model for three-dimensional wind farm simulations for isolating the effects of wind veer and ABL depth. The model is derived from the horizontal momentum equations, and follows both Rossby- and Reynolds number similarity; use of such similarity reduces computation time and allows rational comparison between different conditions. The proposed ABL model compares well with solutions of the mean momentum equations that include wind veer, if the forcing variable is employed as a free parameter.

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Lidar measurements of yawed-wind-turbine wakes: characterization and validation of analytical models

Cited by 30 publications

References 34 publications

Wake steering of multirotor wind turbines

Wake steering of multirotor wind turbines

Optimal Pitch Angle Strategy for Energy Maximization in Offshore Wind Farms Considering Gaussian Wake Model

A pressure-driven atmospheric boundary layer model satisfying Rossby- and Reynolds number similarity

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