CFD Simulations of Flows in a Wind Farm in Complex Terrain and Comparisons to Measurements

Sessarego, Matias; Shen, Wen Zhong; Laan, P. van der; Hansen, Kurt Schaldemose; Zhu, Weijun

doi:10.3390/app8050788

Cited by 35 publications

(25 citation statements)

References 17 publications

(27 reference statements)

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“…Modelling the wake effects of wind turbines is essential for AEP estimation, but quite challenging for wind farms in complex terrain due to the complex interplay between terrain and wake flow [22]. While the CFD can model the wake flow field of a wind farm in complex terrain with a reasonable accuracy [23], its high computational cost makes it unsuitable for wind farm design optimization, since typical optimization algorithms require a large number of design evaluations.…”

Section: Wake Modelmentioning

confidence: 99%

An Optimization Framework for Wind Farm Design in Complex Terrain

Feng

Shen

2018

Applied Sciences

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Designing wind farms in complex terrain is an important task, especially for countries with a large portion of complex terrain territory. To tackle this task, an optimization framework is developed in this study, which combines the solution from a wind resource assessment tool, an engineering wake model adapted for complex terrain, and an advanced wind farm layout optimization algorithm. Various realistic constraints are modelled and considered, such as the inclusive and exclusive boundaries, minimal distances between turbines, and specific requirements on wind resource and terrain conditions. The default objective function in this framework is the total net annual energy production (AEP) of the wind farm, and the Random Search algorithm is employed to solve the optimization problem. A new algorithm called Heuristic Fill is also developed in this study to find good initial layouts for optimizing wind farms in complex terrain. The ability of the framework is demonstrated in a case study based on a real wind farm with 25 turbines in complex terrain. Results show that the framework can find a better design, with 2.70% higher net AEP than the original design, while keeping the occupied area and minimal distance between turbines at the same level. Comparison with two popular algorithms (Particle Swarm Optimization and Genetic Algorithm) also shows the superiority of the Random Search algorithm.

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Section: Wake Modelmentioning

confidence: 99%

An Optimization Framework for Wind Farm Design in Complex Terrain

Feng

Shen

2018

Applied Sciences

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“…Specifically, the Nuclear and Industrial Wind Power Generation Facilities Based on the Electricity Business Act and is relevant for examining safety in terms of wind turbine structures, it was specified by the Nuclear and Industrial Safety Agency that "wind pressure" is to be calculated by taking into account wind conditions at a wind turbine site that include extreme values of wind speed and turbulence fluctuations in the streamwise, spanwise, and vertical directions at the hub height of a wind turbine (June 27, 2014). As a result of this clarification on the use of wind conditions (turbulence) at a wind power generation facility, there is no doubt that the prediction and evaluation of turbulence intensity over complex terrain by CFD (computational fluid dynamics) such as LES and RANS will become increasingly important [3][4][5][6][7][8][9][10][11][12][13][14] . Generally, in RANS models, such as k -ε models, the standard deviation of the streamwise wind velocity which is attributable to terrain and/or surface roughness, Surf u …”

Section: Fig1 Photos Of the Blade Damagementioning

confidence: 99%

Numerical Reproducibility of Terrain-induced Turbulence in Complex Terrain by Large-eddy Simulation (LES) Technique

Uchida¹

2018

Preprint

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In the present study, field observation wind data from the time of the wind turbine blade damage accident on Shiratakiyama Wind Farm were analyzed in detail. In parallel, high-resolution LES turbulence simulations were performed in order to examine the model’s ability to numerically reproduce terrain-induced turbulence. The comparison of the observed and simulated time series (1 second average values) from a 10 minute period from the time of the accident led to the conclusion that the settings of the horizontal grid resolution and time increment are important to numerically reproduce the terrain-induced turbulence that caused the wind turbine blade damage accident on Shiratakiyama Wind Farm. A spectral analysis of the same set of observed and simulated data revealed that the simulated data reproduced the energy cascade of the actual terrain-induced turbulence well.

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“…Utmost caution is required, especially for wind power stations with complicated topography, in planning an optimum arrangement 2 of 27 of wind turbines and controlling both maintenance and management. Diversified tasks are increasingly important to avoid accumulated fatigue of wind load in wind turbines due to terrain-induced turbulence, to reduce malfunctions and accidents inside and outside wind turbines, and to improve the availability of wind turbines [1][2][3][4][5][6][7][8][9][10][11]. Considering social and engineering requests at present, the crucial purpose of the present study is to establish a system with a numerical diagnostic technique for wind status, which contributes to the proper operation of wind farms, an adequate understanding of the indigenous wind environments of each site, including terrain-induced turbulence, and a reduction of malfunctions and accidents associated with wind turbines [12][13][14][15][16][17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

New Assessment Scales for Evaluating the Degree of Risk of Wind Turbine Blade Damage Caused by Terrain-Induced Turbulence

Uchida

Kawashima²

2019

Energies

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The present study scrutinized the impacts of terrain-induced turbulence on wind turbine blades, examining measurement data regarding wind conditions and the strains of wind turbine blades. Furthermore, we performed a high-resolution large-eddy simulation (LES) and identified the three-dimensional airflow structures of terrain-induced turbulence. Based on the LES results, we defined the Uchida-Kawashima Scale_1 (the U-K scale_1), which is a turbulence evaluation index, and clarified the existence of the terrain-induced turbulence quantitatively. The threshold value of the U-K scale_1 was determined as 0.2, and this index was confirmed to not be dependent on the inflow profile, the influence of the horizontal grid resolution, and the influence of the computed azimuth. In addition, we defined the Uchida-Kawashima Scale_2 (the U-K scale_2), which is a fatigue damage evaluation index based on the measurement data and the design value obtained by DNV GL’s Bladed. DNV GL (Det Norske Veritas Germanischer Lloyed) is a third party certification body in Norway, and Bladed has been the industry standard aero-elastic wind turbine modeling software. Using the U-K scale_2, the following results were revealed: the U-K scale_2 was 0.86 < 1.0 (within the designed value) in the case of northerly wind, and the U-K scale_2 was 1.60 > 1.0 (exceeding the designed value) in the case of easterly wind. As a result, it was revealed that the blades of the target wind turbine were directly and strongly affected by terrain-induced turbulence when easterly winds occurred.

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CFD Simulations of Flows in a Wind Farm in Complex Terrain and Comparisons to Measurements

Cited by 35 publications

References 17 publications

An Optimization Framework for Wind Farm Design in Complex Terrain

An Optimization Framework for Wind Farm Design in Complex Terrain

Numerical Reproducibility of Terrain-induced Turbulence in Complex Terrain by Large-eddy Simulation (LES) Technique

New Assessment Scales for Evaluating the Degree of Risk of Wind Turbine Blade Damage Caused by Terrain-Induced Turbulence

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