validated adjustment of remote sensing bias in complex terrain using cfd michael harris, ian locker,...
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Validated adjustment of remote sensing bias in complex terrain using CFD
Michael Harris, Ian Locker, Neil Douglas, Romain Girault, Claude Abiven, Oisin Brady
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What is complex terrain?Terrain likely to perturb the flow from uniformity at turbine level
Steep slopes can perturb the flow. They can be associated with:
- high inflow angle- high veer- recirculation zones- high wind shear- high turbulence
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What is complex terrain?
Forested areas can perturb the flow. They can be associated with:
- high wind shear- high turbulence- amplified risks of recirculation when combined with slopes
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Principles of laser anemometry
3. Line-of-sight windcomponent induces
Doppler shift
2. Aerosols move in the same direction and at same speed as the wind
1. Laser radiation scatters from atmospheric aerosols
4. Detection of Doppler shifted light
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Conically-scanned lidar wind profiler
• Assumes uniform flow across disc at each height
• Obtains line-of-sight wind component as a function of azimuth angle
• Least-squares 3-parameter fit to sine wave
• Horizontal wind speed and direction, vertical wind speed, turbulence
• Builds up wind profile in 20 seconds by measuring wind speed at chosen heights
• Positioning close to mast is no problem
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Complex terrain & inhomogeneous flow• Non-uniform flow will lead to uncertainty for both lidar and mast measurement• Full wind vector requires 3 lidars (expensive), or there are more pragmatic solutions• Volume sampling and easy movement of lidar may be advantageous• 50 points per revolution samples non-uniform flow around disk
Uniform flow Evolving, non-uniform flow
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Hill top measurement: example of flow distortion
Lidar
Conical scan patternStream line
Flow inclination reduces line-of-sight component in both upwind and downwind directions Lidar under-reads compared to mast
Bias is similar for any value of cone angle
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Multiple lidar solution: Windscanner (Risø DTU)
• Three ground-based lidar systems
• Coordinated scanners direct beams to single point in space
• Derive 3D wind vector
• Investigate detailed flow by scanning intersection point – needs rapid data rates
ZephIR 1 ZephIR 2ZephIR 3
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A pragmatic, single lidar approach: adjustment of bias using Ventos CFD modelling
• Follows method of Bingøl et al (this used linear model LINCOM/WAsP Engineering)• Use CFD to model the flow over the terrain for 36 (10-degree) sectors• Use results to calculate mean Doppler shifts observed by lidar around its measurement disk• Perform least squares fit to obtain the predicted lidar wind speed for that sector• Hence calculate ratio of lidar/mast wind speed () – this provides a sector-wise adjustment factor for lidar data at this location• Compare predicted adjustment with actual values of from lidar/mast measurements
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Two approaches are used to model the flow at the scale of a wind farm:- linear models and CFD models (Navier-Stokes equations solvers)
Linear models (e.g. LINCOM , used in WAsP) solve simplified equations, with the following implications:
- break down in case of steep slopes- do not accurately model forests
Advantages of CFD models:- better treatment of turbulence, and inflow angle- detection of recirculation zones- time-dependent computations
Modelling – CFD vs Linear
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Case study 1: lidar beside mast on top of a rounded hill
• IEC compliant mast (M1) with cups at 43m and 60m• ZephIR lidar (Z1) positioned 8m to south• 3 months concurrent wind data • All sectors represented in wind rose• RIX values indicate high ruggedness to W and E
Z1M1
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Sector-wise comparison of predicted and measured differences between lidar and mast
0.85
0.90
0.95
1.00
1.05
1.10
1.15
0 20 40 60 80 100 120 140 160 180 200 220 240 260 280 300 320 340
λ WAsP Engineering 60m
λ ZephIR measurements 60m
λ Ventos 60m
Mast shadowing of cup
General under-read, particularlyin E and W sectors
Wind direction (degrees)
Win
d sp
eed
ratio
(lid
ar/m
ast)
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Lidar vs mast correlation plots – before and after adjustment
• ZephIR measurements adjusted with Ventos
• Unadjusted ZephIR measurements vs mast data• 10-minute average horizontal wind speed• 3% lidar under-read
y = 0.9711xR² = 0.9642
0
5
10
15
20
25
30
0 5 10 15 20 25 30
ZephIR not adjusted
ZephIR not Corrected
Y=x
Linear (ZephIR not Corrected)
y = 0.9982xR² = 0.9644
0
5
10
15
20
25
30
0 5 10 15 20 25 30
ZephIR adjusted
ZephIR Corrected
Y=x
Linear (ZephIR Corrected)
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Variation of wind speed in close vicinity of lidar and mast
• Ventos CFD modelling for 270-degree wind bearing, 60m agl
• Black circle is ZephIR scan disk
• Positions A and B are 50m to E and W of mast
• Mean wind at A is ~2.6% higher than at B
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Other case studies
• Different terrain types examined: forest with clearings, etc.
• Dependence on height above ground level investigated
• Spatial variation of wind speed for different wind directions
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Case study 1• Close to rounded summit• On edge of low tree line with clearings
0.900
0.950
1.000
1.050
1.100
1.150
0 30 60 90 120 150 180 210 240 270 300 330 360
λ Ventos 60mMast 100m/0mmast 200m/0m
1km
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Case study 2• On side of hill in clearing• Tree heights above 15m
0.8
0.85
0.9
0.95
1
1.05
1.1
1.15
1.2
0 30 60 90 120 150 180 210 240 270 300 330 360
λ Ventos 60mMast 100m/0mmast 200m/0m
1km
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Conclusions• Analysis demonstrates high level of agreement between prediction from Ventos CFD and measured impact of flow distortion on lidar wind speed
– Required adjustment to lidar data (2%-5%) is similar magnitude to changes when mast is moved fairly small distance (~100m)
• CFD gives closer agreement than linear model, particularly for sectors with high RIX value
• Proposed methodology combining lidar and CFD in complex terrain– Adjust lidar data for impact of non-uniform flow– Investigate variations across site to reduce uncertainties of overall assessment
