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Evaluation of two novel wake models in offshore wind farms

Jorge Garza(*)(1), Andreas Blatt(1), Rémi Gandoin(1) and Shiu-Yeung Hui(1)

(1) DONG Energy Renewables. Nesa Allé 1, 2820 Gentofte, Denmark

(*) Corresponding author, jorca@dongenergy.dk

Summary

Two novel CFD based wake models, Fuga

by Risø and WindModeller by ANSYS,

along with a DONG Energy implementation

of the N.O. Jensen model, are tested

against quality filtered measurements from

4 offshore wind farms. The comparison is

done for rows of turbines and overall wake

losses.

WindModeller was found to have the best

agreement with the measurements in

narrow direction sectors, while the accuracy

of Fuga and the N.O. Jensen model

improved as the sector was widened to 30°.

For the only case where all three models

were tested in a 30° sector with 1°

resolution, WindModeller and Fuga reached

the same mean absolute error, which was

lower than that for the N.O. Jensen model.

The assessment of the model accuracy in

the estimation of overall wake losses was

only done for two wind farms. The N.O.

Jensen model was found to perform best

regarding the mean signed error, balancing

out over- and underestimations of the wake

losses. All models resulted in errors lower

than 3%.

1. Introduction

With the increasing size and scale for

offshore wind farms and wind farm clusters,

the accurate assessment of power losses

due to wake effects is becoming an

increasingly vital part of overall project

economics. The traditionally used wake

models are simple and to some extent

unphysical, and their operational frame

could be challenged in large wind farm

clusters.

This paper presents a comparison of two

wake models, Fuga [1] and WindModeller

[2], recently developed by Risø and ANSYS

respectively, as a result of the Carbon Trust

Offshore Wind Accelerator (OWA), along

with the widely used N.O. Jensen model

[3,4]. The benchmarking is performed with

two different approaches. The first is the

assessment of losses in wind direction

cases aligned with rows of turbines, which

has been widely studied in the literature.

The second approach combines results

from all directions similarly to the prevalent

commercially available wind farm simulation

software, resulting in estimates of the

overall park wake losses. The model results

are then validated against measurements

from a vast database comprising 4 offshore

wind farms in different areas. The

combination of the two different approaches

builds a bridge between model accuracy in

very specific settings, and its robustness

and suitability for integration into statistical

estimation tools.

This benchmarking study outlines the

current state-of-the-art of wake modelling

by assessing the capabilities of the two new

models using two types of analysis: single

direction and overall park losses. The

combined understanding of these aspects

is crucial for accurately judging which

model performs best. These results

potentially reduce the uncertainty of layout

design.

2. Methodology

The assessment of wake losses has

traditionally been presented as the turbine

efficiency profile along an aligned row of

turbines for a given wind speed and

direction, normalized by the production of

the turbine in the free stream [5,6]. This

stresses the critical decrease in power

production from the first to the second

turbine in a row for aligned turbines, and is

useful to gain insight into the single wake

interaction. It is, however, not directly

translatable to an indication of the overall

wind farm efficiency. This can be done by

extending the analysis in a way analogous

to the traditional methods used for wind

farm yield estimates. This section will go

through a description of the models, the

measurements that are used to evaluate

them and the method in which this is done.

2.1 Models

The comparison is performed for the full

CFD code WindModeller from ANSYS and

linearised CFD model Fuga from Risø, two

novel wake models whose development

has been accelerated by the OWA

programme. Both models are compared to

an internal DONG Energy implementation

of the N.O. Jensen model which is

prevalent in the industry.

2.1.1 N. O. Jensen

The N. O. Jensen single-wake model was

first presented by Jensen [3] and Kátic [4].

For a given rotor diameter and a free

stream wind speed, it predicts the wind

speed deficit as a function of the distance

downstream of the turbine. The wake

expansion is controlled by the wake decay

constant k. For offshore cases, k is

commonly set to 0.04. The magnitude of

the wind speed deficit depends on the

thrust coefficient. The total wake deficit for

a given turbine location is calculated as the

square root of the sum of squares of the

wake deficit induced by all upstream

turbines.

The N. O. Jensen model is implemented as

an in-house tool, which allows the user to

create results for the entire wind rose, with

a resolution of 1° and at a fixed wind speed.

The typical computation time is around 10

seconds.

2.1.2 WindModeller

WindModeller (release 28/06/2011) is a

series of pre- and post- processing scripts

for Ansys CFX. It uses an actuator disc to

model the wake with a momentum sink

(constant thrust per volume at the rotor disk

location), see [2]. It does not take

atmospheric stability into account.

WindModeller can also be used with

varying roughness and orography for

onshore cases.

The computational domain is a cylinder

centred on the wind farm. It has a diameter

of the order of 20km, and a height of 1km.

The grid is refined around the wind farm

area, to a minimal resolution of 30m

horizontally, and 2m vertically. The mesh

around the rotor is adapted during the

solver stage with the actuator disk

parameters. The mesh generation in

WindModeller implies the advantage of

having only one initial mesh, regardless of

the wind direction simulated.

A steady state RANS approach using k-ε

turbulence model is chosen. The constant

Cµ (ratio of surface shear stress and

turbulence kinetic energy) is set to 0.03 in

order to match the upstream turbulence

conditions (upstream turbulence intensity

set to 0.06). The turbulence model setup is

in accordance with the setup used in [2],

considering a turbulence decay exponent of

0.6. A constant roughness length of z0 =

0.0001m is used.

WindModeller operates with CFX (Ansys

13) on a 48GB RAM machine with two

CPUs of 2.3GHz 6-cores each. The

maximum number of iterations is set to 200,

and the residual target to 1e-5. Indicative

computational time and mesh sizes are

given in Table 1.

Wind

Farm

Initial mesh

nodes [106]

Final mesh

nodes [106]

CPU

time (*)

[min]

HR1

1.4

3.43

56

Burbo Bank

0.98

2.12

26

Barrow

1.42

3.09

40

GFS

1.48

3.38

57

Table 1: Initial and final mesh sizes and

indicative computational time for the four

wind farms. (*) for 8m/s.

2.1.3 Fuga

The linearized CFD code Fuga uses the

wind farm layout and wind turbine

parameters in WAsP formats as inputs. It is

designed for sites with homogeneous

terrain and roughness.

The model solves the time independent

RANS equations with a simple closure

scheme for turbulence and a mixed spectral

solver using pre-calculated look-up tables

[1]. The model neglects the Coriolis force

and buoyancy effects and, due to the latter,

it can only model neutral atmospheric

stratification. An actuator disk is used to

model the wake and the flow is lid-driven,

with the height of the upper boundary zi,

where the velocity is fixed, being one of the

user inputs.

Preliminary-look-up tables (pre-LUTs) are

created once during installation of the

software, and LUTs are created for each

case (turbine + z0 + zi combination). This

results in a very fast computation of the

results, in the order of 1 minute per case.

Fuga version 1.51 is used for this paper. All

simulations were run with a value of

zi=500m and a surface roughness length of

z0=0.0001m. The extents of the maps used

to create the wind farm files were similar to

those used for WindModeller simulations.

2.2 Measurements

SCADA data from 4 offshore wind farms

are available for the analysis, in the form of

10-minute averaged values. The wind

farms under consideration are Horns Rev 1,

Gunfleet Sands, Burbo Bank and Barrow.

Their locations are shown in Figure 1.

The vast database offers a wide span of

test cases, both regarding the turbine type,

turbine spacing and thrust coefficient

values, which are the main drivers behind

wake loss calculation. A big variety

regarding the symmetry of the layouts can

also be found. While all layouts are regular

arrays, two are symmetric and two are

asymmetric. This adds another interesting

dimension by investigating the effect of

layout symmetry on model accuracy. All this

gives a large set of results for turbine rows,

from which only selected cases are shown

here.

For transect analysis, in order to isolate

wake effects from other possible power

losses, measurements are filtered for ideal

operating conditions in a manner inspired

by [7]. This is achieved by removing

records where turbine availability within the

Figure 1: Location of the wind farms used in

the analysis.

10-minute period was not perfect, along

with records in which the turbine controller

imposed a limitation to the turbine power

output. Due to the rounding of the data in

the database, perfect availability is defined

as 95% or more of the 10-minute period

operating correctly, and not exclusively

100%. Furthermore, for every individual

case, only records with the specific wind

speed and direction are used.

For overall wake losses calculations, the

data quality filter greatly reduces the

amount of available data, since all turbines

are now required to be operating correctly

at the same time. In order to maximise the

amount of data available, the filter criteria is

modified and a record is deemed

acceptable if more than 95% of the turbines

were operating correctly. This means 77 out

of 80 turbines in Horns Rev 1 and 46 out of

48 in Gunfleet Sands. This was found to

increase the amount of available data for

the analysis by a factor of 6 to 8, and its

effect on the general trend of the results

was small. This relaxation of the filter

criteria can add uncertainties to the

analysis, since it is assumed that the

missing turbines are distributed uniformly,

while it might not necessarily be the case.

This uncertainty was accepted due to the

great benefit of a drastic increase in the

data sample size.

Since the models produce one result per

wind direction and the measurements can

provide many data points for each direction,

with population distributions that are not

necessarily uniform, the data averaging

applied to the measurements is not trivial. A

simple average would assign the same

weight to every single data point, which

could in practice mean that the observed

wake losses at X° wind direction are given

a higher weight than those at Y°, depending

on the number of observations at each

direction; while averaging of model results

gives the same weight to all directions. In

order to avoid this unequal weight, a

smoothing method is applied by first

averaging all measurements at every 1°

wind direction and then averaging the

smoothed 1° results falling in the sector in

turn.

2.3 Method

The SCADA data is quality-filtered by

removing the effects of availability and

power curtailment, in order to isolate wake

losses. The wind direction is obtained from

the yaw alignment signals of all available

turbines, a procedure that is verified with

met mast data when available. A set of

reference, free stream turbines is defined

according to the wind direction. Finally, the

free stream wind speed is derived from the

power production at the reference along

with the density corrected power curve. The

method for correcting power curves for air

density is verified with density-specific

power curves from several manufacturers.

For the analysis of a row of turbines, the

power from the turbines in the row is

normalized by that of the reference turbine

to create efficiency profiles. For wind farm

efficiency, the power from all the wind farm

is divided by the product of the number of

turbines and the reference power. Then the

efficiency of the wind farm at a given wind

speed is calculated for all directions,

grouped in 30° sectors or other angular

resolution. The contributions from each

sector are then scaled according to a wind

rose and their sum results in the wind farm

efficiency at that wind speed.

For this process to be accurate, the sample

size for all wind speed - direction

combinations should be sufficiently large.

This data should be representative of the

general climatology of the site including

atmospheric stability, a factor that is not

explicitly included in the calculations but is

known to have an effect on wake losses

[8,9].

The models are used as recommended by

the software developers, and part of the

analysis focuses on the reproducibility of

previously published results, in order to

assess how dependent are the models to

the user.

Results from Fuga and N.O. Jensen

models are produced for 1° bins and then

averaged linearly in the different 30° sector

sizes. This places WindModeller on a

different ground from the others, since it is

computationally expensive and impractical

to run simulations for the whole wind rose

at 1° resolution. Therefore, in the cases of

rows of turbines, WindModeller results are

referred to the average of results every 1°

in a certain directional sector, while in the

overall wake losses only the results for the

sector centre angle are taken into account.

This work does not aim at testing the

models on all speed – direction

combinations, therefore the total wind farm

wake losses are not considered.

3. Results

3.1 Rows of turbines

3.1.1 Horns Rev 1

Horns Rev 1 provides here the longest

datasets. It has been extensively used for

previous studies [5,7,8,9], and is

investigated here as a starting point.

Figure 2 shows the results for a row of 10

turbines with 7 rotor diameter (RD) spacing

and 10m/s winds from 270°. They are

shown for specific direction, 10° and 30°

wide sectors (centre angle ± 5° and ± 15°,

respectively). WindModeller is the closest to

the measurements in the 1° and 10° sector

cases. The accuracy of Fuga and N.O.

Jensen improves as the direction sector is

widened to 30°, where both Fuga and

WindModeller have excellent predictions.

3.1.2 Other wind farms

The same analysis was performed for rows

of turbines in other wind farms which have

not been previously used for this kind of

studies. Namely Gunfleet Sands in the

North Sea; Burbo Bank and Barrow in the

Irish sea.

Figure 2: Results for wake losses along a row of turbines in Horns Rev 1 for winds from 270° ± a)

1°, b) 5° and c) 15°. 10 WTG/ and 7D spacing

Figure 3: Results for wake losses along a row of turbines in Gunfleet Sands for winds from 239° ±

a) 5° and b) 15°. 9WTG and 8.4D spacing

T02 T12 T22 T32 T42 T52 T62 T72 T82 T92

0

0.2

0.4

0.6

0.8

1

POW [-]

270deg - 10m/s

Measurements

FUGA

N.O. Jensen

WindModeller

01deg bin

T02 T12 T22 T32 T42 T52 T62 T72 T82 T92

0

0.2

0.4

0.6

0.8

1

POW [-]

270deg - 10m/s

Measurements

FUGA

N.O. Jensen

WindModeller

10deg bin

T02 T12 T22 T32 T42 T52 T62 T72 T82 T92

0

0.2

0.4

0.6

0.8

1

POW [-]

270deg - 10m/s

Measurements

FUGA

N.O. Jensen

WindModeller

30deg bin

G01 G02 G03 G04 G05 G06 G07 G08 G09

0

0.2

0.4

0.6

0.8

1

POW [-]

239deg - 08m/s

Measurements

FUGA

N.O. Jensen

WindModeller

10deg bin

G01 G02 G03 G04 G05 G06 G07 G08 G09

0

0.2

0.4

0.6

0.8

1

POW [-]

239deg - 08m/s

Measurements

FUGA

N.O. Jensen

30deg bin

Figure 4: Results for wake losses along a row of turbines in Burbo Bank for winds from 313° ± a) 5°

and b) 15°. 8WTG, 5.3D spacing

Figure 5: Results for wake losses along a row of turbines in Barrow for winds from 312° ± a) 5° and

b) 15°. 8WTG, 5.5D spacing

T38 T37 T36 T35 T34 T33 T32 T31

0

0.2

0.4

0.6

0.8

1

POW [-]

313deg - 08m/s

Measurements

FUGA

N.O. Jensen

30deg bin

T38 T37 T36 T35 T34 T33 T32 T31

0

0.2

0.4

0.6

0.8

1

POW [-]

313deg - 08m/s

Measurements

FUGA

N.O. Jensen

WindModeller

10deg bin

B8 B7 B6 B5 B4 B3 B2 B1

0

0.2

0.4

0.6

0.8

1

POW [-]

312deg - 08m/s

Measurements

FUGA

N.O. Jensen

WindModeller

10deg bin

B8 B7 B6 B5 B4 B3 B2 B1

0

0.2

0.4

0.6

0.8

1

POW [-]

312deg - 08m/s

Measurements

FUGA

N.O. Jensen

30deg bin

Results for a test case at 8m/s along the

longest row in each wind farm are shown in

Figure 3 for Gunfleet Sands (9 turbines with

8.4RD spacing), Figure 4 for Burbo Bank (8

turbines with 5.3RD spacing) and Figure 5

for Barrow (8 turbines with 5.5RD spacing).

The result for the specific 1° directional bins

has been omitted, since its practical value

is limited when considering the

uncertainties in measured wind direction

within a 10 minute period. It was not

possible to produce runs every 1° for

WindModeller in all cases due to limited

time and computational resources; and

these plots therefore do not show results for

this model in 30° sectors.

The same general trend as for Horns Rev 1

is observed, with WindModeller having the

best accuracy for smaller sector widths.

Fuga and N.O. Jensen improve their

accuracies as the sector widens to 30°.

For Gunfleet Sands, WindModeller is very

accurate at 10°, slightly overpredicting

wake losses; while the other two models

overpredict the losses by a wider margin.

When switching to a 30° sector, N.O.

Jensen captures the behaviour at the first

three waked turbines and then

progressively underpredicts losses, while

Fuga underpredicts all along but gets the

last turbine correct.

For the cases of Burbo Bank and Barrow,

all models were inaccurate in the 10° case

and all overpredict the wake losses.

WindModeller could capture the losses at

the first waked turbine, but not the

development of the loses along the row.

Fuga and N.O. Jensen improved when

averaging over 30°; with N.O. Jensen

performing best in the Burbo Bank test case

and both performing equally well in Barrow.

Fuga was found to underpredict the losses

in both cases.

3.1.3 Rows of turbines – General

It was observed that in general terms, Fuga

and N.O. Jensen accuracy increases

notably with increasing directional sector

bin size; while WindModeller accuracy

improves as well, but in a much less

dramatic way.

In the Horns Rev 1 case, where the full

span of 0° to 30° averaging sector width

could be tested, both new models were

found to reach comparable levels of

accuracy for 30° sectors, achieving mean

absolute error values lower than 1%. The

relationship between mean absolute error

and averaging sector width for the Horns

Rev 1 case is shown in Figure 6.

Figure 6: Evolution of mean absolute error

for all three models as a function of the width

of the direction sector in Horns Rev 1 with

10m/s winds from 270°.

3.2 Overall wake losses

Wake losses along a perfectly aligned row

of turbines has been studied extensively in

wake modelling research. For its application

in energy yield estimates, the most

important is the accuracy of the wake

model when integrated with wind statistics

binned in (typically) 30° sectors and

weighted with the corresponding wind

speed and wind direction frequency. The

estimation of wake model accuracy when

predicting overall wind farm wake losses is

part of the bridge between the in-row wake

loss analysis and its application in industry

standard procedures.

The smoothing technique of averaging the

measurements in every 1° and then

averaging all 1° results in the sector was

not applied in this case, since it would

drastically reduce the amount of data

available for the analysis as well as adding

uncertainties as a result. The metric for

comparing the models is the mean signed

error. This is considered the most sensitive

approach since in the case of a direct

application to yield assessment, the fact

0 5 10 15 20 25 30

0

0.05

0.1

0.15

0.2

0.25

Average Absolute Errror [-]

Sector Width [deg]

FUGA

N.O. Jensen

WindModeller

that positive errors can cancel out negative

errors is relevant.

Figure 7 shows the results for wind farm

efficiency as a function of wind direction

every 10°, predicted by all three models

and from measurements at Horns Rev 1

and 10m/s. It shows that all models capture

the general trend, including the directions

with the largest losses and those with peak

efficiency. It is also seen that, for some

particular directions, all models disagree

with measurements. It was verified that

model error does not correlate with the

amount of data in each sector, which

indicates that there was enough data to

perform the calculation and the error stems

from other factors.

Figure 7: Wind farm efficiency as a function

of wind direction every 10°, predicted by all

three models and from measured data from

Horns Rev 1 at 10m/s.

It is worth stressing that the overall wake

loss results presented are obtained with the

wind rose of the final filtered dataset. It is

not representative of a full year. Moreover,

these results are given for one wind speed

value and are therefore not representative

of the total wind farm wake losses.

Table 2 shows the resulting mean signed

error for all models in two test cases where

enough measurements were available and

WindModeller simulations with a 30°

resolution were performed: Horns Rev 1

with 10m/s wind speed and Gunfleet Sands

with 8m/s wind speed. Furthermore, 10°

WindModeller simulations were available

for the Horns Rev 1 case, and an additional

field is included in the table for this case. It

is found that all models perform well, within

3% error.

Case

Fuga

N.O.J.

WM

HR1-30,

10m/s

1.4%

0.7%

-0.1%

HR1-10,

10m/s

2.1%

1.5%

3.0%

GFS-30,

8m/s

2.5%

1.8%

-1.2%

Table 2: Mean signed error in overall wake

losses for the three models in three test

cases in Horns Rev 1 and Gunfleet Sands.

4. Discussion

Models are easy to use. In the N.O. Jensen

model, the user can only change the wake

decay constant, which makes it very robust

and well suited to the wind analysts

community. Fuga has a limited amount of

parameters that can be tuned, namely the

height of the boundary layer (forcing), the

roughness length and the averaging

method. Considering that the height of the

boundary layer does not drastically

influence the results if it is set high enough,

the influence of user is limited to the

averaging method and the roughness

length. This makes Fuga and the N.O.

Jensen very simple and easily reproducible.

In contrast, like any industrial CFD code,

WindModeller has to be used with caution

due to the multitude of user defined

parameters. The turbulence model setup is

very well documented [2], but the user has

to check grid and numerical convergence.

The results obtained for the analysis of

wake losses along a row of turbines in

Horns Rev 1 confirm the findings of the

OWA and previously published studies, for

instance [10]. The comparison of wake

models with 1° transect results are found to

be irrelevant, because of the significant

scatter in the wind direction measurements.

In addition, unsteady flow features like

wake meandering are beyond the capability

of the models in this study.

Fuga and Windmodeller are the closest to

the measurements for the 10 and 30° bin

width. The absolute error for each of the

models decreases with the bin width

(Figure 6). This is especially true for Fuga

which shows similar errors as WindModeller

when the width exceeds 10°. These values

allow the population of records to be

representative enough with regards to the

030 60 90 120 150 180 210 240 270 300 330

0.4

0.5

0.6

0.7

0.8

0.9

1

1.1

Wind Direction [deg]

POW[-]

Meas.

FUGA

N.O. Jensen

WindModeller

model assumptions (steadiness,

especially).

Similar comments can be made on the

three other wind farms, with different wind

speed, Ct, and spacing values. With a 10°

bin width, WindModeller comes first, Fuga

second, and N.O. Jensen third. One could

then expect similar behaviour with other

regular arrays. The CFD codes are better

suited to capture lateral turbine interaction,

which in the case of regular arrays is

important [6]. This could explain the better

accuracy of Fuga and WindModeller when

looking at 10deg bins. When comparing on

30deg bins, the N.O. Jensen performs well

in comparison to Fuga, and nothing

indicates that one model should be

preferred.

Regarding the propagation of the wake

losses along a row of turbine, one can

observe that the pattern is different for each

wind farm. For Horns Rev 1 and Gunfleet

Sands, the losses keep decreasing,

whereas in Barrow and Burbo the efficiency

slightly increases after the second turbine,

and then decreases. This is especially

visible with 10° averaged bin size. One

could suggest this is due to the low turbine

spacing combined with the high thrust at

8m/s. Fuga seems to be the only method

properly capturing this pattern.

Due to the high computational resources

required, it is unfeasible and undesirable to

run 360-1° simulations for yield estimate

and layout optimisation purposes.

Therefore Ansys WindModeller is not

equally represented as the others because

simulations have not been run for every 1°.

It might be missing a smoothing effect the

others have, but the results from the HR1-

30° resolution case (Figure 6) suggest that

this effect would not be as drastic as with

Fuga.

Regarding overall wake losses (Figure 7),

the three models seem to capture the

general trend in some directions, but not all.

In some sectors, all models are very close

to each other, yet none replicates perfectly

the measurements.

The pattern of wake losses per sector is

symmetric for the models in HR1, which is a

symmetric layout. The measurements,

however, do not show this perfect

symmetry. The east half of the wind rose

shows higher wake losses than the

western. This could be due to both the fact

that easterly and westerly winds in this

location are climatologically different, or to

the fact that the data subset used to build

these statistics is not symmetric regarding

the content of different turbulence levels or

atmospheric stability conditions. For

example, the sectors between 110°-130°

are clearly less efficient than those between

290°-310°, and the proportion of stable

conditions in these two directions is found

to be very different (75% for the range

110°-130° against 20% for the range 290°-

310°).

The most sensible way to compare the

models is with the mean error in overall

wake losses, rather than the mean absolute

error. This is closer to what would be done

in an energy yield estimate, where the

positive errors in a sector are combined

with the negative errors in another one. It is

complicated to pick a definite best with

these results, but a clear conclusion is that

under this analysis, the N.O. Jensen model

is found to be robust and perform as well as

the novel, more complicated models.

After commenting on the results, it is worth

stressing that comparing wake models with

power measurements involves strong

filtering of the data: for availability, wind

speed, etc. In order to maximise the

amount of data, additional filters like

stability, turbulence, speed up effects

around the wind farm, or unsteady

situations within 10min period have not

been taken into account. These problems

can be counter-balanced by a high number

of representative samples, in each of the

wind direction bins. And indeed, similar

results are obtained on all wind farms,

although they are in different climates and

equipped with different machines. However,

the interaction between stability and layout

efficiency, both varying with the wind

direction, needs to be better understood.

5. Conclusion

The OWA results have been replicated

independently on a similar but not identical

dataset without a concurrent on-site met

mast for the full measurement period. The

relevance of single degree transects can be

questioned, since it exceeds both

measurements (yaw precision) and model

limitations (steadiness).

From the previously unpublished

comparison with three wind farms in the

North Sea and the Irish Sea, WindModeller

and Fuga perform better than N.O. Jensen,

and give comparable results when the wind

direction bin size exceeds 10°. CFD models

are likely to better capture the lateral

interaction of wakes in regular arrays.

When comparing with 30° bins, all models

perform equally well.

A way of assessing model accuracy in

overall wake losses has been proposed.

The N.O. Jensen model is robust: despite

its simplicity it withstands the comparison

and proves to be useful even when

compared to much more complex models.

The data filtering focused on maximising

the amount of measurements. Thus, the

dataset contains various stability conditions,

as well as other complex flow features. This

study focuses on including three new wind

farms to wake model comparisons and

extending these comparisons to overall

wake losses, instead of investigating the

effect of these features on the

measurements. Future work should focus

on better understanding the interaction

betwen stability, turbulence, availability,

park speed-up and inter-park effects.

6. References

[1] Ott, S. Linearized CFD. Risø-I-3093.

January 2011.

[2] Montavon C. et al. Offshore wind

Accelerator: Wake modelling using CFD.

EWEA 2011, Brussels.

[3] Jensen, N.O. A note on Wind Generator

Interaction. Risø-M-2411, Risø National

Laboratory, 1984, Roskilde, Denmark

[4] Katic, I., Højstrup, J., Jensen, N.O.. A

Simple Model for Model for Cluster

Efficiency. EWEC 1986 Proceedings, 7-9

October 1986, Rome.

[5] Méchali M. et al. Wake effects at Horns

Rev and their influence on energy

production. EWEC 2006, Athens.

[6] Barthelmie, R.J. et al. Quantifying the

Impact of Wind Turbine Wakes on Power

Output at Offshore Wind Farms. Journal of

Atmospheric and Oceanic Technology,

2010.

[7] Barthelmie, R.J. et. al. Wp8: Flow.

Deliverable D8.1 Data. Wake

measurements used in the model

evaluation. UpWind Project.

[8] Jensen, L. Array efficiency at Horns Rev

and the effect of atmospheric stability.

EWEA 2007, Milan.

[9] Hansen, K. et al. The impact of

turbulence intensity and atmospheric

stability on power deficits due to wind

turbine wakes at Horns Rev wind farm.

Wind Energy Journal, 2011.

[10] Gribben, B. Wake effect development

in the Offshore Wind Accelerator. EWEA

Workshop, Brussels, 2011.