Lidar Measurement Accuracy under Complex Wind Flow in Use for Wind Farm Projects Matthieu Boquet, Mehdi Machta, Jean-Marc Thevenoud mboquet@leosphere.fr

Agenda Remote sensors in the framework of a wind farm project Assessment of accuracy and uncertainty: statistical approach Application on WINDCUBE™ v2 lidar data sets

Assessing the wind resource for a wind farm project The future production of a wind farm depends on:  the wind across the site and over project life  the performance of the turbine array Determining the future production (mean value and its uncertainty) drives the financing of the project  The better the analysis (i.e. the lower the uncertainty) the more attractive the financial terms Use measurements, know-how, theories, models, experience…

The need for measurements Vertical data at hub height Horizontal data cross the site ?

Application of remote sensor Remote sensors measure up to and above hub height Remote sensors are portable and can therefore be easily moved to measure at several locations They allow a more secure assessment of the wind resource... …But only if the remote sensor is providing accurate wind data with a high confidence level! Before applying one technology rather than another, the instrument’s accuracy and uncertainty need to be well understood and assessed.

Instrument comparison with a calibrated cup WINDCUBE® v2 lidar Reference calibrated cup anemometer Y = 0.996 X + 0.08 (m/s) R2 = 0.998 Mean deviation = 0.5% Standard deviation of deviation = 2.1% Will you get same bias (accuracy) and scatter (uncertainty) on your site of application? Yes, if the technology is robust Yes, if you were lucky and have the exact same measurement conditions on the site of application than during this test No, otherwise…

Accuracy needs to be thoroughly assessed By technological knowledge: Technology used: radio, acoustic, light… Device configuration: emission, reception… Device conception: material, electronic… Signal processing: algorithms… By empirical analysis: Considered as a black box Accuracy assessed under a wide (exhaustive?) variety of conditions Approach of IEC 61400-12-1* standard for wind turbine power curve measurements * Currently under revision

Statistical approach Knowing the device technology, all potentially influential parameters need to be listed (according to knowledge, literature, experience, etc.): Wind shear, wind veer, turbulence intensity… Atmospheric stability, rain… Aerosol concentration, ambient noise… These parameters need to be measured during the accuracy/uncertainty assessment test The relationship between wind speed deviations (between the remote sensor and the calibrated cup) and the parameters are assessed

A classifier is used to assess these relationships error distribution during sensitivity test Test site conditions during test + Real error (m.s-1) Classifier is found (decision tree, etc.) Estimated error (m.s-1) General site conditions Classifier

Statistical approach One classifier is built per data set using a statistical methodology One data set of variables is built, following the probability distributions defined for every variable and thus can be considered representative for most sites of application The method of building the classifiers is valid if they give same RSD error distribution for the same general data set This error distribution allows for estimation of the RSD mean bias and uncertainty

Remarks on the statistical approach The classifier needs to take into account dependency between variables It determines the most influential parameters on the deviations in the data set Be careful with variables known to be influential which are not present during the test! Parameter variations on site of application could be also addressed as input to the classifier

Application to WINDCUBE® v2 lidar Here classifier is a decision tree bag It estimates system error based on atmospheric and instrumental parameters The classifier predicts the instrument error!

Error distribution for several data sets (taken under various conditions) Estimated error (m.s-1) Classifiers are found for various data sets and applied to reference site: results of estimated accuracy are coherent among the data sets

Influential parameters and final accuracy The most influential parameters are found to be wind shear and flow inclination (expected from theory) Precipitation, turbulence intensity, aerosol concentration are found to have an inconsequential influence on the accuracy Accuracy is defined as the mean error and is here found to be ~1% Uncertainty is defined as the standard deviation of the error and is here found to be lower than 2% Results are coherent with validations against met masts, as seen in slide 6: Y = 0.996 X + 0.08 (m/s) R2 = 0.998 Mean deviation = 0.5% Standard deviation of deviation = 2.1%

Conclusion The accuracy of a remote sensor technology needs to be assessed according to environmental parameters in order to ensure that the measurement will not differ from a validation test site to the application site WINDCUBE® Lidar has shown high robustness toward surrounding environmental variables and its accuracy has been assessed in a large variety of sites and conditions The instrument’s measured wind speed values (and the uncertainty associated) can be used for assessment of the wind resource on wind farm sites, resulting in a high lidar RoI