Wake model benchmarkingusing LiDAR wake measurements of multi MW turbinesStefan Kern, Clarissa Belloni, Christian

Aalburg

GE Global Research, Munich

GE Power & Water

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Motivation• Today’s wake models developed in the

80’s

• Environment changed significantly– Increased turbine size

– Very large plants on- & offshore

• Continuous calibration/improvement of models

• Lack of data for velocity deficit and turbulence intensity in wake of today’s wind turbines

ENDOW project Vindeby wind farm, Bonus 450kW (stall regulated)

38m

35m

100m

100m

GE 2.5xl, 2.5MW

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Outline

• Measurement setup

• Challenges of wake measurements with a LiDAR

• Measurements of turbulence intensity with LiDAR

• Velocity deficit models used in benchmarking study

• Benchmarking results for velocity deficit of a single turbine

• Summary & conclusions

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Measurement setup

WindCube LiDAR

with 15° prism

Upstream metmast H=100m, cup/sonic

anemometers GE2.5xl, 2.5MW

D=100m HH=100M

downstream

distance 2–8 D

200 hours of data obtained over period of 3 months

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Measuring wakes with LiDARChallenges• 4 measurements with temporal and spatial

offset combined • assumption of homogeneous flow

Opening angle of scanning cone• WindCube offers 30° & 15°• 15° preferred for horizontal shear expected

in present measurements

LiDAR orientation• Alignment with wake direction minimizes

systematic errors

α

15° 30°

87m40m

100 m

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Measuring turbulence intensity with LiDAR Free stream conditions• LiDAR with 30° prism placed next to met-mast• Acceptable correlation of TI

Waked conditions • LiDAR with 15° prism placed next to met-mast• Upstream distance to turbine 2.5D• LiDAR measurements show largely increased TI• Large scatter Li

DA

R T

urb

ule

nce

[%

]Met mast Turbulence [%]

LiD

AR

Turb

ule

nce

[%

]

Met mast Turbulence [%]0 105 15 20 250

5

10

15

20

25

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What scales of wind speed fluctuations can LiDAR capture?Statistics of free stream wind speed fluctuations wind speed

increments

Current system not suitable to measure wake turbulence

Height

Probability density of ∆u for different τ and measurement heights

45m

85m

108m

τ=1.5s τ=12s τ=96sLiDAR LiDAR LiDARMet mast Met mastMet mast

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Benchmarked wake modelsJensen model (aka Park model)•Linear wake expansion•Uniform velocity in wake

Ainslie eddy viscosity model•Axisymm. shearlayer approximation of NS

eq.•Eddy viscosity closure

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Vertical velocity profilesWind speed ~10m/s, medium ambient TI

2D downstream, wake center ±5°

Free stream (MM)Wake (LIDAR & uncertainty)AinslieJensen

8D downstream , wake center ±3°

Free stream (MM)Wake (LIDAR) AinslieJensen

Jensen model to be used with care for close turbine spacing

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Lateral velocity profiles at hub height

Small wind speedLarge thrust coefficient

Medium wind speedMedium thrust coefficient

Large wind speedSmall thrust coefficient

Sector averaged wake velocities 6D downstream, medium ambient TI

Wake (LIDAR) AinslieJensen

Wake (LIDAR) AinslieJensen

Models differ mainly at wake center, improvements needed for large wind speeds

Wake (LIDAR) AinslieJensen

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Velocity deficit at hub height

Small wind speedLarge thrust coefficient

Medium wind speedMedium thrust coefficient

Large wind speedSmall thrust coefficient

Velocity deficit at wake center vs. downstream distance, medium ambient TI

Wake (LIDAR) AinslieJensen

Wake (LIDAR) AinslieJensen

Wake (LIDAR) AinslieJensen

Model improvements needed for large wind speeds

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Results summary

•High quality data aquired for wide range of wind conditions

•Overall, Ainslie outperforms Jensen model (in-line with results from others)

•Model accuracy varies with wind conditions

•Relatively small differences between the models for partial wakes/rotor averaged wake velocities

•Average error of wake affected velocity ~7% at 6D downstream (waked 100% of the time, which is typically not the case)

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Conclusions•LiDAR measurements provide high quality velocity deficit data

for calibration of wake velocity deficit models

•Current LiDAR technology captures only large scale wind speed fluctuations correctly turbulence intensity measurements not recommended

•Present benchmarking results enable

−Systematic improvement of velocity deficit models

−Appropriate choice of model for specific application/wind conditions