Ground-based remote sensor uncertainty – a case study for a wind lidar

1. Ground-based remote sensor uncertainty – a case study for a wind lidar A. Albers1, K. Franke1, R. Wagner2, M. Courtney2, M. Boquet3 1Deutsche WindGuard Consulting GmbH, Varel, Germany(a.albers@windguard.de) 2 DTU Wind Energy, Roskilde, Denmark (rozn@dtu.dk) 3LEOSPHERE SAS, Paris, France (mboquet@leosphere.fr)

2. Fundamental Requirements onRemote Sensing Applications,CD IEC 61400-12-1, Ed. 2

3. Test Case • Same lidar unit tested by WindGuard and RISØ DTU • Type of lidar: Windcube V2

4. 1. Verification Test Provides Traceability to National Standards Rysum 104m Hovsore 100m • By comparison to traceably calibrated reference sensors on mast in flat terrain • No Correction/Calibration foreseen • Analysis focuses on bin averages • Similar results in Rysum and Hovsore, deviations may be explained by differences in cup anemometer measurements

5. 2. Sensitivity TestExample Wind Shear Rysum (104m) Hovsore (100m) • Problem:Environmental conditions during application of the lidar/sodar deviate from environmental conditions during Verification Test • Solution:Type specific sensitivity of lidar/sodar error on environmental variables needs to be investigated • Slopes observed in Rysum and Hovsore are mostly of same order of magnitude (not in example shown above)

6. 2. ClassificationRanges of Variables • Complex terrain application not foreseen in CD IEC 61400-12-1, Ed. 2

7. 2. ClassificationMax. Influence of Variables • The maximum influence of each variable on the wind speed measurement is calculated on the basis of the Sensitivity Test:

8. 2. ClassificationSignificance and Correlation of Variables • Only environmental variables covering a sufficient range are considered • Only significant environmental variables are considered • Correlation of environmental variables needs to be taken care of for avoiding double counting • Partly different variables remain at the test sites Rysum and Hovsore • The influences of the remaining variables are cumulated to a possible total error

9. 2. Accuracy Classes • Outlier results observed at 40m at Hovsore assumed being due to mast effects on reference sensors. • The class numbers represent maximum errors. • The high class numbers are partly due to the high ranges of variables. • Solution: consider only mean deviation of the environmental variables at the application of the lidar/sodar and at the Verification Test- often much lower uncertainties than application of class number- recommended in revision of IEC 61400-12-1

10. 1. Verification Test,Random Noise Error Rysum 104m Hovsore 100m • Random Noise Error: Part of the scatter which cannot be explained by sensitivities to environmental variables • Very similar result for Rysum and Hovsore, mostly <1% • Uncertainty only relevant for single 10-minute periods, uncertainty very low for bin averages and automatically integrated in statistical uncertainty of bin averages (e.g. power curves, site assessments)

11. 3. Control of Lidar at Application with Small Met Mast • Check on obvious outlier data or malfunctioning • Check whether systematic deviations of lidar and control anemometer are in expected range under consideration of uncertainties of reference measurements and the sensitivities of the lidar: - feed-back algorithm: additional uncertainty if criteria not met - helps to avoid overoptimistic lidar classifications • Check whether scatter of deviations of lidar and control anemometer are as expected: - additional uncertainty if criteria not met, however only relevant in terms of single 10-minute periods, uncertainty very low in terms of bin averages (automatically integrated) • In-situ test of lidar (test on changes of accuracy within measurement period)

12. 4.d Inhomogeneous Airflow Over Probe Volumes • Assumption: equal wind conditions in different probe volumes • Significant problem in complex terrain for all lidars and sodars commercially available today and key reason for not accepting lidar/sodar in complex terrain by IEC 61400-12-1 MT • Analysis of uncertainty by flow model or Mann Bingöl approach Mann Bingöl Model

13. Examples for Total Uncertainty • quite similar total uncertainties resulting from both test sites

14. Conclusions • Results of Verification Tests of the same lidar unit quite consistent at Rysum and Hovsore • Clear and consistent trends observed in Sensitivity Tests for some important variables, e.g. wind shear • Partly inconsistent results in Sensitivity Tests due to problems of: - sufficient range coverage - significance criteria - correlation among environmental variables - mast effects on reference measurements • Methodology provides realistic uncertainty ranges • An Accuracy Class in the range of 3 to 6 seems being realistic for the Windcube V2, leading typically to a standard uncertainty in terms of wind speed of about 1-2% in practice. • The total standard uncertainty of the wind speed measurements is often in the range 2-3% for a Windcube V2. • The evaluated lidar uncertainty is by definition higher than the uncertainty of reference cup anemometers.