Wake model benchmarking using LiDAR wake measurements of multi MW turbines Stefan Kern, Clarissa Belloni, Christian Aalburg GE Global Research, Munich GE Power & Water

2 / GE EWEC 2010 / 5/5/2015 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 GE 2.5xl, 2.5MW

3 / GE EWEC 2010 / 5/5/2015 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

4 / GE EWEC 2010 / 5/5/2015 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

5 / GE EWEC 2010 / 5/5/2015 Measuring wakes with LiDAR Challenges 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° 87m 40m 100 m

6 / GE EWEC 2010 / 5/5/2015 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 LiDAR Turbulence [%] Met mast Turbulence [%] LiDAR Turbulence [%] Met mast Turbulence [%]

7 / GE EWEC 2010 / 5/5/2015 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=96s LiDAR Met mast

8 / GE EWEC 2010 / 5/5/2015 Benchmarked wake models Jensen model (aka Park model) Linear wake expansion Uniform velocity in wake Ainslie eddy viscosity model Axisymm. shearlayer approximation of NS eq. Eddy viscosity closure

9 / GE EWEC 2010 / 5/5/2015 Vertical velocity profiles Wind speed ~10m/s, medium ambient TI 2D downstream, wake center ±5° Free stream (MM) Wake (LIDAR & uncertainty) Ainslie Jensen 8D downstream, wake center ±3° Free stream (MM) Wake (LIDAR) Ainslie Jensen  Jensen model to be used with care for close turbine spacing

10 / GE EWEC 2010 / 5/5/2015 Lateral velocity profiles at hub height Small wind speed Large thrust coefficient Medium wind speed Medium thrust coefficient Large wind speed Small thrust coefficient Sector averaged wake velocities 6D downstream, medium ambient TI Wake (LIDAR) Ainslie Jensen Wake (LIDAR) Ainslie Jensen  Models differ mainly at wake center, improvements needed for large wind speeds Wake (LIDAR) Ainslie Jensen

11 / GE EWEC 2010 / 5/5/2015 Velocity deficit at hub height Small wind speed Large thrust coefficient Medium wind speed Medium thrust coefficient Large wind speed Small thrust coefficient Velocity deficit at wake center vs. downstream distance, medium ambient TI Wake (LIDAR) Ainslie Jensen Wake (LIDAR) Ainslie Jensen Wake (LIDAR) Ainslie Jensen  Model improvements needed for large wind speeds

12 / GE EWEC 2010 / 5/5/2015 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)

13 / GE EWEC 2010 / 5/5/2015 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