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Wednesday, 12 March 2014
16:30 - 18:00 Wakes: Do we need different models for onshore and offshore wind farms?
Resource Assessment  


Room: Ponent
Session description

The accurate prediction of the wake effects within and between wind farms is vital to wind farm design and to provide a prediction of the energy output. To date, the vast majority of wind farms have been designed using engineering models which have been tuned and validated using experimental data. As wind farms become larger, empirical correction upon empirical correction are being developed upon the basis of scarce and perhaps erroneous experimental data. Perhaps this is the appropriate time to question if this is the right and only approach.

In the session advanced models and observations will be described and discussed.

Learning objectives:

  • Describe what wakes are, how they can be seen in observations and how they are modelled
  • Identify different models, state-of-the-art and more classical ones
  • See wakes in observations and understand the related issues
Lead Session Chair:
Lars Landberg, DNV GL – Energy (Garrad Hassan), Denmark
Hauke Beck Forwind - University of Oldenburg, Germany
Co-authors:
Hauke Beck (1) F P
(1) Forwind - University of Oldenburg, Oldenburg, Germany

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Presenter's biography

Biographies are supplied directly by presenters at EWEA 2014 and are published here unedited

Mr. Beck has been researching for one and a half years in wind energy as a research assistant at ForWind Oldenburg, Germany. He studied aeronautical and aerospace technology at the University of Applied Science in Bremen and advanced this with a degree in Engineering Physics of the Carl-von-Ossietzky University of Oldenburg. His research is currently focused on wake behavior of wind energy turbines.

Abstract

The Ainslie wake model - an update for multi megawatt turbines based on state-of-the-art wake scanning techniques

Introduction

J.F. Ainslie presented his engineering wake model 25 years ago. Although several physically more appropriate models exist, still this relative simple and robust model is widely used. It is applied for energy yield prediction at typical turbine spacings of 2D to 5D for modern large turbines. In the industry’s practice the model is provisionally tuned by experience and by a few number of point measurements of wind speeds or power. In contrast, state-of-the art remote sensing devices offer to validate wake flow models by direct flow measurements of the velocity deficit and its relaxation along the entire wake deployment.

Approach

Two independent onshore and offshore measurement campaigns of 5MW and 6MW wind turbines of two manufacturers were performed. Two long-range-all-sky-scanner LiDAR devices measured the near (<2D) and far wake (>2D) of turbines in partial and full load operation with the low elevation PPI scan mode (Plan Position Indicator) at various atmospheric turbulence levels. The scans were averaged over ten minutes and compared with simulations of an implementation of the Ainslie model with consideration of similar atmospheric conditions.

Main body of abstract

The individual line-of-sight-velocities of the LiDAR measurements were projected on the averaged wind direction measured by a nearby met mast. At a distance of two rotor diameters downstream the horizontal velocity profile was extracted and normalized with the free inflow profile. This wake profile was used for the initialization of the classic Ainslie model which was used to simulate the rotationally symmetric stationary wake deployment. This simulated wind fields were analyzed in the same manner (compensate inclination, cutting plane) as the LiDAR measurements and were compared to the experimental results in the area of interest. It turned out that up to a distance of 5D, the simulations underestimated the deficit for the comparison of both campaigns by using the classic parameters of the Ainslie model. From this position on, a good accordance can be found. By adjusting the atmospheric turbulence intensity towards lower values a good agreement with the measurements can also be found up to 5D, but quite poor agreement for higher distances. In contrast the parameter k1 and the transition function ƒ clearly improved the performance of the model in the entire area of interest.

Conclusion

For future projects dealing with wakes, the modified version of the Ainslie model is recommended to determine steady wakes in good precision. It will be and has to be investigated how much further validations of the model will be needed. Especially under the aspect of multiple wake set-ups the question arises who realistic and applicable this version of the model will be.


Learning objectives
Ainslies classic wake model can still be used as a serviceable method to simulate steady single wakes for modern wind converters but has to be modified to map the behavior of wakes in detail and to achieve more reliable results.