Albers, Axel

Courtney, Michael

Gottschall, Julia

Lindelöw, Per Jonas Petter

Online Research Database In Technology

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Downloaded from orbit.dtu.dk on: Dec 17, 2017Application-oriented classification of lidar profilers - or: Introducing lidars to powerperformanceGottschall, Julia; Courtney, Michael; Lindelöw, Per Jonas Petter; Albers, AxelPublication date:2010Document VersionPublisher's PDF, also known as Version of recordLink back to DTU OrbitCitation (APA):Gottschall, J., Courtney, M., Lindelöw, P. J. P., & Albers, A. (2010). Application-oriented classification of lidarprofilers - or: Introducing lidars to power performance. Poster session presented at 2010 European Wind EnergyConference and Exhibition, Warsaw, Poland.Cl ifi ti f lid filass ca on o ar pro ers  PO IDh t i t d lid t f t ti. or: ow o n ro uce ars o power per ormance es ng311        Julia Gottschall Mike Courtney1) Axel Albers2) ,  ,  1)Risø DTU 2)Deutsche WindGuard Consulting GmbH   Summary Concept of lidar verification Commercially available lidars have reached a level ofaccuracy where they can be considered as seriousalternative to standard cup anemometers – especiallyith i ifi t d t i l h i ht iw s gn can a van ages n arge e g s or n areaswhere it is difficult and costly to set up a measuring mast.A further benefit is that lidars can measure wind speedsand wind directions simultaneously at different height Figure 1: Setup of verification test meteorologicallevels why they are particularly suited for the   –t i d ith f d d,t f ti l i d hmas  equ ppe w re erence sensors an  groun -measuremen o ver ca w n s ear. based lidar Blue crosses show the locations of the.        reference sensors red arro s indicate theA standardized application to power performance testing  ,  w  ( ll ll t) measuring volume of the lidar.as we as more genera y resource assessmen ,   however requires a traceable classification scheme that,allows for a complete evaluation of the uncertainty of themeasurements performed by the lidar. Verification test (= comparison of lidar measurements to traceable reference sensors ie cup anemometers     ,  The procedure, we propose, is based on the verification and wind vanes, at different heights and under a set of pre-defined conditions)of a ground based lidar profiler against a tall        - → analysis of data in terms of different regression models as basis for lidar calibrationmeteorological mast that is equipped with reference          ( ti f t ti bi )t diff t h i ht l l F ll i thcorrec on or sys ema c assensors a eren e g eve s. o ow ng el ti f lid ( d i ti b t lid d f t) b i f l ifi tirecommendations for the evaluation of cup anemometers → eva ua on o  ar error = ev a on e ween ar an  re erence measuremen  as as s or c ass ca on(uncertainty due to operational characteristics)in IEC 61400-12-1, the verification test is analysed in    terms of both a calibration and a classification of thetested instrument In this way traceability is transferred Figure 2: Results for different regression. ,f th f t th t t d lid d  rom e re erence sensors o e es e ar an a models – (left) 1-parametric regression respective lidar uncertainty is deduced accordingly with and without offset.     ( C + k d lidTh k t d h h b i d t t fy =    x an  y = m x, resp.; y: are wor presen e ere as een carr e ou as par o wind speed, x: reference wind speed);the EU FP6 UpWind project (WP 6) and is directly      (right) 2 parametric regression -   connected to IEC MT12-1 currently revising the IEC (z = D + ku x + kg y; z: lidar wind speed 61400 12 1 standard for power performance testing error x: reference wind speed y: wind- - . ,    ,   d di t)I 2009 f thi th Lid A t P j tspee  gra en .n , or s purpose, e ar ccep ance ro ec wasinitiated to organize the work on lidar testing and classification For more details about the data the testissues related to IEC standard revision in a satellite group   ,   . site and the procedures of analysis seeRef 3. .DiscussionEstimation of uncertainties The presented lidar verification scheme prepares basis forFigure 3: Uncertainty components of cup anemometer measurements t bl lid t (t th f )   - racea e ar measuremen s o e re erence sensorsaccording to IEC 61400-12-1 (with typical numbers in parentheses) – - repeatable lidar measurements (with respect to a wellu : uncertainty of anemometer calibration (0 14 m/s)V1,i   .  , defined uncertainty)uV2 i: uncertainty due to operational characteristics / classification a consistent evaluation of uncertianties (in line with IEC,(0 05 m/s + 0 005 u )·k / √3; k = 1 31) -.    .  i     . ,t i t f fl di t ti d t ti ff t (1%) 61400-12-1 and GUM)uV3 i: uncer a n y o  ow s or on ue o moun ng e ec s ,.,uV4 i: uncertainty of flow distortion due to terrain (2%),      ,u : uncertainty in data acquisition system ( ); Results of the verification test are evaluated as calibrationdV,i     -uV i = (uV1 i2 + uV2 i2 + uV3 i2 + uV4 i2 + udV i2)1/2 and as classification at the same time – with the drawbacks,  ,  ,  ,  ,  , .that the calibration functions depend on the specific externalconditions and only a certain range of the possible conditionsi d b th l ifi tis covere y e c asss ca on.U t i t b d t f lid tncer a n y u ge  or ar measuremen s: The lidar calibration is based on a reference that is itselff t i t i bi d t i t f associated with a significant uncertainty A necessary re erence uncer a n y, e com ne uncer a n y o  cup .anemometer measurements (u ≡ u ) assumption is that the reference is un-biased and that theref Vt i ti t i ll di t ib t d ( f R f 2) lidar calibration uncertainty defined by uncertainty of uncer a n es are symme r ca y s r u e c . e . .–   calibration function (if calibration is applied; ulid l )  . ca . Lidar calibrations depend not only on the actual site lidar classification uncertainty – estimated by [∑ ε /(N-1)]1/2 conditions (eg vertical wind shear turbulence) but also on the i i( lid ) i h d f l,εi: ar error  w t respect to covere range o  externa measurement height Extrapolations between verification testconditions (u ).d li ti t b id dlid. class. an app ca on are o e cons ere .( 2 2 2 )1/2ulid i = uref i + ulid cal i + ulid class i  S ( h) b f b i., , . ., . ., ome roug num ers rom our o servat ons:+ additional uncertainty due to mounting / setup of lidar ulid l ≈ 0 2 m/s without calibration → 0 1 m/s with calibration;     + additional uncertainty due to specific conditions during application. c ass. . .d i ti d t i t l diti h i ht t l ti   ev a ons ue o vary ng ex erna con ons or e g ex rapo a onsnot covered by verification test 0.01-0.02 m/s (but also extreme cases with larger deviations);u negiligible (for all three introduced models)lid. cal.Figure 4: Standard deviation (blue arrows) and square root of second R f          e erencesmoment (as estimate for symmetric uncertainty; red arrows) of lidar error for individual wind speed bins – (top) before and (bottom) after calibration 1 IEC 2005 Wi d t bi P t 12 1 P f t f l t i it           f lid i d d l l i th i f ti (h lt.   n  ur nes – ar  - : ower per ormance measuremen s o  e ec r c yd i i d t bi (IEC 61400 12 1 I t ti l t d d)o  ar w n  spee  va ues app y ng e regress on unc ons ere: resu s pro uc ng w n ur nes  - -  n erna ona  s an arfor 2-parametric model) 2. ISO 1995 Guide to the Expression of uncertainty in measurements (GUM), DS/ENV 13005  .3. M Courtney, R Wagner, P. Lindelöw 2008 IOP Conf. Series: Earth and EnvironmentalScience 1, 012021European Wind Energy Conference & Exhibition 2010, Tuesday 20 - Friday 23 April 2010, Warsaw, Poland              

Application-oriented classification of lidar profilers - or: Introducing lidars to power performance

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Application-oriented classification of lidar profilers - or: Introducing lidars to power performance

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