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Analysis of long distance wakes behind a row of turbines-a parameter study

1) Research Objectives, Research question and Sub-questions

The major aim of this research is to analyse the long distance wake behind the row of 10 turbines in order to forecast wake recovery. For this, the following objectives will be achieved:

  • To determine sensitivity to different parameters including Reynolds number, grid resolution and turbulence characteristics
  • To determine the impact of using different internal turbine distances
  • To study  the  power  production  and  the  velocity  deficit  in  the  farm wake

The below research question will be answered:

  • What is the impact of long distance wakes behind the row of turbines?

2) Condensed Literature Review

Troldborg, et al. (2014) defined that large offshore wind farms support to produce long distance wakes. In like manner, increase in number of offshore wind farms, increases the chances of interaction with one wind farms to other neighbouring wind farms. It enables to wake from one wind farm to another. According to Almazyad, et al. (2014), majorly larger wind farms are planned at the offshore location as it remains the most suitable sites to build the wind farms.

Due to this reason, on these locations, the clusters of wind farm can be seen as these wind farms are constructed near to each other. Nygaard (2014) analysed that behind these wind turbines in the wind farms, there is a disrupted flow of air which is known as wake which can be characterized as the reduced speed of wind while increasing the turbulence.

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The individual turbine wakes are combined with each other for the purpose of forming a farm wake which remains supportive to travel at a longer distance. Moreover, in wind farm, clusters farm to farm interaction also takes place. Due to which, the long distance wake of a wind farm also impacts the wind conditions of other wind farms too which are built in the surrounding area (Witha, et al., 2014).

The wind turbulence works on the Mann model and according to which fluctuating body forces the wind farm upstream. Moreover, under this model, neutral atmosphere is assumed but it is identified that this model remains assistive to study wake effects inside the farms but not support to evaluate longer distances which are needed in the context of farm to farm interaction. To evaluate the long distance wakes, various numerical studies have taken place.

Troldborg, et al. (2014) depicted that every large offshore wind farm has a long distance wake as increased number of offshore wind farms supports to increase more occasions regarding interaction of wake from one wind farm to another. So, it can be stated that long distance wakes directly impact the wind conditions at the neighbouring sites.

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On the other hand, Nygaard (2014) determined that to get the better understanding regarding long distance wakes and a row of turbines, there is a need of predicting the wake recovery in an accurate manner. In this context, numerical simulations as well as measurements in the wake remain assistive. In the perspective of earlier studies, it is identified that the wake can be done by utilizing simplified wake models.

It includes the models which are applied for the purpose of momentum equation, roughness elements while representing the turbines. According to the views of Almazyad, et al. (2014), there is a quantifiable relationship between wind farm efficiency and wind speed. In like manner, the direction of the turbines, turbulence and atmospheric stability also play vital role in the generation of power output.

Moreover, Witha, et al. (2014) identified that wake losses are majorly take place due to strong wind speed variations in the context of turbine thrust coefficient; direction, atmospheric stability and turbulence plays vital role. In like manner, the efficiency of wind farm remains highly dependent on the distribution of wind speeds as well as wind direction.

Eriksson, et al. (2014) illustrated that the impact of turbine spacing towards wake losses is highly uncertain in nature and in this perspective, it is identified that there is an evidence of deep array effect which reflects that wake losses in the centre of the wind farm are generally remains under-estimated while focusing towards wind farm model. However, it is identified that the overall efficiency of the wind farm can be evaluated on the basis of prediction as it compensates the edge effects.

3) Methodology

For this research study, long term wind data from a wind farm will be collected from the measurements available at 3 km and 6 km east of the wind farm for a row of 10 turbines. These data sets are presented in UPWIND report. All these data sets have 10 minutes mean values.

For analysing this data, analytical analysis will be used by using the SPSS technique that will help to provide the descriptive statistics and regression. Apart from this, simulation method will also be preferred to estimate the production in the farm and velocity deficit at different distances of wakes. The simulations will be conducted according to the turbulence and wind shear conditions of the site. There will be a comparison between the measured production data and wind data in the wake.

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References

Almazyad, A. S., Seddiq, Y. M., Alotaibi, A. M., Al-Nasheri, A. Y., BenSaleh, M. S., Obeid, A. M., & Qasim, S. M. (2014). A proposed scalable design and simulation of wireless sensor network-based long-distance water pipeline leakage monitoring system. Sensors14(2), 3557-3577.

Eriksson, O., Nilsson, K., Breton, S. P., & Ivanell, S. (2014). Analysis of long distance wakes behind a row of turbines–a parameter study. In Journal of Physics: Conference Series (Vol. 524, No. 1, p. 012152). IOP Publishing.

Nygaard, N. G. (2014). Wakes in very large wind farms and the effect of neighbouring wind farms. In Journal of Physics: Conference Series (Vol. 524, No. 1, p. 012162). IOP Publishing.

Troldborg, N., Sørensen, J. N., Mikkelsen, R., & Sørensen, N. N. (2014). A simple atmospheric boundary layer model applied to large eddy simulations of wind turbine wakes. Wind Energy17(4), 657-669.

Witha, B., Steinfeld, G., Dörenkämper, M., & Heinemann, D. (2014). Large-eddy simulation of multiple wakes in offshore wind farms. In Journal of Physics: Conference Series (Vol. 555, No. 1, p. 012108). IOP Publishing.

 

 

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