Post conversion of Lidar data on complex terrains
1 like589 views
The document discusses the challenges and methodologies for post-conversion of lidar data in complex terrains, highlighting the need for precise remote sensing devices. It emphasizes the importance of utilizing computational fluid dynamics (CFD) for effective conversion factors that enhance the agreement between lidar and standard anemometer data. The findings suggest that post-conversion improves measurement accuracy, especially in highly complex terrains, bringing correlation factors closer to unity.
Post conversion of Lidar data on complex terrains
- 1. Post conversion of Lidar data on complex terrains Stéphane SANQUER (1), Alex WOODWARD (2) (1) Meteodyn France, (2) ZephIR Lidar 27/09/2016 LIDARs - the zapping competition – WIND EUROPE SUMMIT 2016 1Stéphane SANQUER
- 2. 229/09/2016 Context… Wind turbines generator higher and higher Need Remote Sensing Devices (RSD) precise enough to be compared to standard anemometers Strong inhomogeneity of wind on complex terrains Some discrepancies may appear with standard anemometers Post conversion of Lidar data in such situations Numerical methodology to give efficient post conversion factor May we use CFD ? When should we use the post-conversion : level of terrain complexity ? Challenges of forest modelling – WIND EUROPE SUMMIT 2016 Context and purposes
- 3. Why conversion are needed in complex terrains ? Velocity vectors aren’t be constant across the Lidar scan disk We need the 3D vectors of the wind upstream and downstream the Lidar scan disk center in order to project them on the beams 329/09/2016Challenges of forest modelling – WIND EUROPE SUMMIT 2016 Context and purposes 𝑉𝐿 = 𝑉𝑟 𝑑𝑜𝑤𝑛 − 𝑉𝑟 𝑢𝑝 2 sin(𝜃) = 𝑉𝑑 . 𝑖 − 𝑉𝑢 . 𝑗 2 sin(𝜃) 1.00 1.05 1.10 1.15 1.20 0 1 2 3 4 5 Correctionfactor Wind incidence (°) 𝑉𝐿 = 1 2 sin(𝜃) 𝑉𝑑 sin 𝜃 + 𝜑 𝑑 + 𝑉𝑢 sin(𝜃 − 𝜑 𝑢 ) CF=VC/VL
- 4. 429/09/2016Challenges of forest modelling – WIND EUROPE SUMMIT 2016 Interpretation of site complexity F. Bingöl (2009) : Complex terrain and wind Lidars – Risoe-PhD Context and purposes
- 5. 529/09/2016Challenges of forest modelling – WIND EUROPE SUMMIT 2016 Context and purposes Differences between RSD and mast data depends on complexity
- 6. 629/09/2016Challenges of forest modelling – WIND EUROPE SUMMIT 2016 Context and purposes Purposes… Assessing differences between ZephIR 300 and mast data in various complexity classes Highlighting the categories where CFD conversion is needed Providing an efficient methodology for conversion of Lidar data on complex terrains
- 7. 729/09/2016Challenges of forest modelling – WIND EUROPE SUMMIT 2016 Computations of 8 sites : from the simplest to the highest complex Assessing the conversion factor Orography Roughness ModeratelycomplexHighlycomplex CFD Conversion Factor 𝑉𝐿 = 1 2 sin(𝜃) 𝑉𝑑 sin 𝜃 + 𝜑 𝑑 + 𝑉𝑢 sin(𝜃 − 𝜑 𝑢 )
- 8. 829/09/2016Challenges of forest modelling – WIND EUROPE SUMMIT 2016 Post-conversion of Lidar data At each time step, by using the conversion factor (depends on the wind incidence) RSD data are Post-converted Correlation factor R² unchanged – Linear regression factor increased close to unity
- 9. 929/09/2016Challenges of forest modelling – WIND EUROPE SUMMIT 2016 Post-conversion of Lidar data 0.86 0.88 0.90 0.92 0.94 0.96 0.98 1.00 Flat terrain Moderately complex Complex Highly complex Before conversion After conversion Linear regression factor ULidar vs UMast
- 10. 1029/09/2016Challenges of forest modelling – WIND EUROPE SUMMIT 2016 Conclusions Applying CFD conversion to data from RSD in complex terrain improves the agreement between wind speed measurements from RSD and masts CFD post conversion needed for complex and extremely complex terrain. Same effect of terrain than forest on the linear regression factor (0.97). Conversion improve the factor close to unity (0.99) After correction for highly complex site, the factor is 0.98. Method of post conversion depends on the ability of CFD to accurately predict the flow deviation over the ground. Post-conversion of Lidar data