1. Ship based -lidar measurements, G. Wolken-Möhlmann et. al., Proceedings of DEWEK 2012,Bremen

2. Simulation of motion induced measurement errors for wind measurements using LIDAR on floating platforms, G.

Wolken-Möhlmann et. al., Proceedings of iSARS 2010, Paris, France

3. Lidars on floating offshore platforms – about the corrections of motion-induced lidar measuremen terrors, J. Gottschall

et. al., Proceedings of EWEA conference 2012, Copenhagen

4. Path towards bankability of floating lidar data, P. Flower, DNV-GL, Presentation EWEA Offshore 2013, Frankfurt

Measuring wind offshore in deep water depths will be a future challenge. Where

the sea bed installation of foundations for fixed met masts is impossible, even the

mooring of floating systems are more complicated. Ship-lidar systems are an

alternative solution for a number of different applications.

In this poster we describe two motion-correction methods for motion-influenced

lidar measurements. The ship-lidar system will be presented as well as the first

measurements carried out as part of the EERA-DTOC project. Therefore a

verification of one correction algorithm will be shown as well as first results from

wake measurements behind the Alpha Ventus offshore wind farm.

First wake measurements were

performed with ship tracks

perpendicular to the wakes, see figure

8. Results show distinct wakes for a

distance of approx. 15D, see figure 7.

These wakes can be identified by

decreased wind speeds and increased

turbulence.

For longer distances, reference wind

data are necessary.

Abstract

First verification test and wake measurement results

using a Ship-LIDAR System G. Wolken-Möhlmann*, J. Gottschall and B. Lange

Fraunhofer IWES, Am Seedeich 45, 27572 Bremerhaven, Germany

*gerrit.wolken-moehlmann@iwes.fraunhofer.de

Measuring set-up

Motion correction algorithms

References

EERA DeepWind 2014, 11th Deep Sea Offshore Wind R&D Conference

The ship-lidar system comprises a Leosphere WindCube V2 device, different motions

sensors (AHRS, satellite compass), a computer for data acquisition as well as equipment

for power supply and wireless communication. The system is combined in a frame in order

to ensure a fixed geometry, compare [1].

For the presented measurements, the system was installed on the offshore support vessel

LEV TAIFUN with a length of 41.45 m. The system was located approx. 7 m above the point

of rotation, whereas the roll frequency is close to f=5 s with extreme roll angles up to 20°

Verification of correction algorithm

Conclusions

First ship-based lidar measurements next to met masts FINO1 show good correlations for

wind speed and direction using the simplified correction. Nevertheless it is assumed that the

complete motion correction will improve the data.

Using the ship-lidar for wake measurements, wakes could be identified clearly for distances of

approx. 15 rotor diameters. For longer distances, inflow reference data as well as a complete

motion correction is necessary.

For the first analysis, the simplified correction algorithm was applied on the measured data.

Figure 5 shows results of uncorrected, yaw corrected (rotated) and fully corrected data for

one-minute-mean values for the 5th of October 2013.

Wind-lidar measurements using line of sight (LoS) measurements in different beam

orientations are solved under the assumption of homogeneity as well as constant wind velocity

on each altitude, using a system of linear equations (SLE):

In general, these measurements can be influenced by translatory and rotatory motions, that

can be considered by modifying the SLE to

Under the assumption of constant orientation and motion during the time period covered by

the system of SLE, a simplified motion correction can be applied on the resulting wind vector

from the SLE.

Especially periodical tilting motions in the frequency range of the lidar measurement

frequency, combined with additional translatory motion due to the distance from lidar to center

of rotation can lead to beating effects that are not considered by the simple motion correction.

Floating lidar corrections algorithms were studied in [2] and [3].

Figure 1: Ship-lidar installation on the LEV

TAIFUN.

Figure 2: Ship-lidar measurement in proximity to

offshore meteorological mast FINO1. Position of

the lidar system is indicated by the red beam lines.

Acknowledgements

This research projekt was funded as part of the

Seventh Framework Programme.

Reference data was provided by the DEWI (wind data) and the German Federal Maritime and Hydrographic

Agency (BSH) which was acquired as part of the FINO project, funded by the German Federal Ministry for the

Environment, Nature Conservation and Nuclear Safety (BMU).

Wake measurements

𝑜𝑥 𝑡1 ⋯ 𝑜𝑧 𝑡1⋮ ⋱ ⋮

𝑜𝑥 𝑡𝑛 ⋯ 𝑜𝑧 𝑡𝑛

∙𝑢 ℎ𝑣 ℎ𝑤 ℎ

=𝑣𝐿𝑜𝑆(𝑡1, ℎ))

⋮𝑣𝐿𝑜𝑆 𝑡𝑛, ℎ

=> 𝑂 ∙ 𝑢 = 𝑣 𝐿𝑜𝑆

𝑂𝑡𝑖𝑙𝑡 ∙ 𝑢 − 𝑣 𝑠𝑦𝑠.𝑉𝑒𝑙𝑜𝑐𝑖𝑡𝑦,𝑙𝑡 = 𝑣 𝐿𝑜𝑆𝑤𝑖𝑛𝑑 + 𝑂𝑡𝑖𝑙𝑡 ∙ 𝑉𝑘𝑜

𝑠𝑦𝑠.𝑉𝑒𝑙𝑜𝑐𝑖𝑡𝑦

𝑢𝑤𝑖𝑛𝑑 = 𝑅𝑦𝑎𝑤 𝑢𝑚𝑒𝑎𝑠𝑢𝑟𝑒𝑑 − 𝑢𝑠ℎ𝑖𝑝

00:00 06:00 12:00 18:00 00:000

5

10

15

20

win

d s

peed [m

/s]

uncorrected

1 min mean reference Fino1

00:00 06:00 12:00 18:00 00:000

5

10

15

20

win

d s

peed [m

/s]

rotated data

1 min mean reference Fino1

00:00 06:00 12:00 18:00 00:000

5

10

15

win

d s

peed [m

/s]

corrected data

1 min mean reference Fino1

00:00 06:00 12:00 18:00 00:00

500

1000

timedis

t. to F

ino1 [m

]

00:00 06:00 12:00 18:00 00:000

100

200

300

400

win

d d

irection [°]

uncorrected data

1 min mean reference Fino1

00:00 06:00 12:00 18:00 00:00100

200

300

400

win

d d

irection [°]

rotated data

1 min mean reference Fino1

00:00 06:00 12:00 18:00 00:00150

200

250

300

350

win

d d

irection [°]

corrected data

1 min mean reference Fino1

00:00 06:00 12:00 18:00 00:00

500

1000

timedis

t. to F

ino1 [m

]

Scatter plots also show improvements

between uncorrected and corrected data

(see figure 6). Nevertheless the correlation

of the data is not comparable to fixed lidar

or lidar-buoy data [4].

A reason could be motion effects on the

ship measurement that are not considered

by the simplified motion correction.

Figure 5: Comparison of wind speed and direction data between FINO1 and ship measurement for different

levels of correction.

Measurement campaigns

For the EU FP7-funded EERA-DTOC

project, two measurement campaigns were

performed from 27-31 August 2013 and 04-

09 October 2013 comprising approx. 7.5

days in proximity to Alpha Ventus wind

farm.

Goal of the measurement was the survey

of wind farm wakes in different distances.

In order to verify the measurement

principle, data was also acquired in free

inflow in proximity to FINO1. -2000 0 2000 4000 6000 8000 1000012000

-8000

-6000

-4000

-2000

0

2000

4000

6000

8000

10000

12000

lon dist to Fino1 [m]

lat

dis

t to

Fin

o1 [

m]

track 05.10.2013

-2000 0 2000 4000 6000 8000 1000012000-8000

-6000

-4000

-2000

0

2000

4000

6000

8000

10000

12000

lon dist to Fino1 [m]

lat

dis

t to

Fin

o1 [

m]

track 06.10.2013

-2000 0 2000 4000 6000 8000 1000012000-8000

-6000

-4000

-2000

0

2000

4000

6000

8000

10000

12000

lon dist to Fino1 [m]

lat

dis

t to

Fin

o1 [

m]

track 07.10.2013

-2000 0 2000 4000 6000 8000 1000012000-8000

-6000

-4000

-2000

0

2000

4000

6000

8000

10000

12000

lon dist to Fino1 [m]

lat

dis

t to

Fin

o1 [

m]

track 08.10.2013

Figure 4: Plots of ship track for the second measurement campaign.

0 1 2 3 4 5 6 7 8 9 100

1

2

3

4

5

6

7

8

9

10

u Fino1, 80 m

u u

nkorr

, 77 m

Uncorrected data vs. corrected data for selected time

y = 1.0029*x , r2 = 0.9906; 127 MPs

uncorrected data

corrected data

Figure 6: Scatter plot of 10-min-mean values,

selected for wind direction and distance to FINO1.

0 1000 2000 3000 4000 5000 6000-4000

-3000

-2000

-1000

0

1000

2000

3000Date: 05.10.2013 , time: 10:39:60 timecode:00012481

LON dist to Fino1 [m]

LA

T d

ist to

Fin

o1 [m

]

Windspeed, 1 minute mean, D= 773.8 m

0 10 20 30 40 5050

100

150

4.5

5

5.5

6

6.5

7

Turbulence intensity for 1 minute mean

0 10 20 30 40 5050

100

150

5

10

15

20

0 10 20 30 40 50 600

5

Roll

[°]

0 10 20 30 40 50 600

2

4

Speed [

m/s

]

Windspeed, 1 minute mean, D = 1800 m

heig

ht [m

]

0 10 20 30 40 50

60

80

100

120

140

160

180

4.5

5

5.5

6

6.5

7

Turbulence intensity for 1 minute mean

time in minutes

heig

ht [m

]

0 10 20 30 40 50

60

80

100

120

140

160

180

6

8

10

12

14

16

18

20

22

24

Figure 7: Plots of wind speed and

turbulence intensity.

Figure 8: Overview of ship track and

theoretical wind wakes.