Borraccino, Antoine

Wagner, Rozenn

Online Research Database In Technology

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Downloaded from orbit.dtu.dk on: Mar 30, 2019Nacelle lidar for power perf. – the UniTTe approach to retrieve VBorraccino, Antoine; Wagner, RozennPublication date:2017Document VersionPublisher's PDF, also known as Version of recordLink back to DTU OrbitCitation (APA):Borraccino, A. (Author), & Wagner, R. (Author). (2017). Nacelle lidar for power perf. – the UniTTe approach toretrieve V. Sound/Visual production (digital)DTU Wind Energy, Technical University of DenmarkNacelle lidar for power perf. –the UniTTe approach to retrieve V∞A. Borraccino, R. WagnerIEA Wind task 32 – workshop nacelle lidars27th September 2017DTU Wind Energy, Technical University of DenmarkPower performance testingThe modern ways2Remote sensing instruments –Future/Now: use of nacelle-based wind lidarsZephIR Dual Mode(scanning)by ZephirLidarWind Iris(4-beam)by AventLidarWind Eye(4-beam)by Windar PhotonicsDiabrezza(9-beam)by Mitsubishi ElectricDTU Wind Energy, Technical University of DenmarkSearching for free stream wind speed3𝑽𝑽∞ ??𝑽𝑽𝟐𝟐.𝟓𝟓𝟓𝟓Modern turbines: 2.5D ~ 200-400m• Decorrelation WSpeed / power• Hub height speed insufficient?• 2.5D not really free wind …DTU Wind Energy, Technical University of Denmark• Method is (not new…)Schlipf D., Rettenmeier A., Haizmann F., Hofsäß M., Courtney M. and Cheng, P. W.:“Model Based Wind Vector Field Reconstruction from Lidar Data”, DEWEK, 2012.• need new “wind models” for profiling nacelle lidars, suitable for power performance testingModel-fitting Wind Field Reconstruction4DTU Wind Energy, Technical University of Denmark5What’s wrong with 2.5D?• Lidar range capabilities–Soon not sufficient for very large turbines–Or systems will become more expensive• Measurement/beam locations–Are/will be too far away• to accept WFR assumptions: inhomogeneity, lack of coherence, etc• nac. lidars measure wind less and less representative to what the turbine feels–Affected by e.g. terrain or site features• ”Decorrelation” issues might come back• AND…–2.5D really is NOT free stream!–Under-estimation of V∞ by about 0.7%• According to models, confirmed by measurements• Should be accounted into AEP calculations…DTU Wind Energy, Technical University of DenmarkWind model accounting for shear6• Use lidar measurements at 2.5 rotor diameters• “static” model: stationarity assumed• Assumes horizontal homogeneity and power law shear profile• Fits three wind characteristics wind speed 𝐕𝐕𝟎𝟎 (@𝐻𝐻ℎ𝑢𝑢𝑢𝑢)+ relative wind dir. 𝜽𝜽𝒓𝒓 (yaw misalignment)+ shear exponent 𝜶𝜶𝒆𝒆𝒆𝒆𝒆𝒆DTU Wind Energy, Technical University of DenmarkCombined wind-induction model7• Use lidar measurements at multiple distances close to rotor• Additionally assumes simple induction model:(from actuator disk and vortex sheet theory)𝑈𝑈 𝑥𝑥𝑈𝑈∞= 1 − 𝑎𝑎𝑖𝑖𝑖𝑖𝑖𝑖 1 + 𝜉𝜉1 + 𝜉𝜉2• Fits four wind characteristics Free stream wind speed 𝐕𝐕∞ (@𝐻𝐻ℎ𝑢𝑢𝑢𝑢) + relative wind dir. 𝜽𝜽𝒓𝒓+ shear exponent 𝜶𝜶𝒆𝒆𝒆𝒆𝒆𝒆+ induction factor 𝒂𝒂𝒊𝒊𝒊𝒊𝒊𝒊DTU Wind Energy, Technical University of DenmarkWind speed resultsMast comparison, WFR using the wind-induction model horizontal speed estimated @hub height and 2.5D_rot  IEC “free sector”: 110°, 219°85B-demo: use the 5 LOS4 dist, from 0.5 to @1.2D_rotZDM: use 6 LOS3 dist., from 0.3 to 1.2D_rot𝒚𝒚 = 𝟎𝟎.𝟗𝟗𝟗𝟗𝟐𝟐𝟗𝟗𝒆𝒆 𝑹𝑹𝟐𝟐 = 𝟎𝟎.𝟗𝟗𝟗𝟗𝟗𝟗𝟓𝟓𝒚𝒚 = 𝟎𝟎.𝟗𝟗𝟗𝟗𝟗𝟗𝟗𝟗𝒆𝒆 − 𝟎𝟎.𝟎𝟎𝟓𝟓𝟗𝟗𝟐𝟐 𝑹𝑹𝟐𝟐 = 𝟎𝟎.𝟗𝟗𝟗𝟗𝟗𝟗𝟓𝟓𝒚𝒚 = 𝟎𝟎.𝟗𝟗𝟗𝟗𝟗𝟗𝟗𝟗𝒆𝒆 𝑹𝑹𝟐𝟐 = 𝟎𝟎.𝟗𝟗𝟗𝟗𝟗𝟗𝟗𝟗𝒚𝒚 = 𝟎𝟎.𝟗𝟗𝟗𝟗𝟓𝟓𝟐𝟐𝒆𝒆 + 𝟎𝟎.𝟎𝟎𝟎𝟎𝟎𝟎𝟗𝟗 𝑹𝑹𝟐𝟐 = 𝟎𝟎.𝟗𝟗𝟗𝟗𝟗𝟗𝟗𝟗DTU Wind Energy, Technical University of Denmark9Measured Power curves (scatter)WFR using wind-induction model5B-demo ZDMMastDTU Wind Energy, Technical University of Denmark10Challenges in PCV with nacelle lidars• Need to give directions to methods for wind fieldreconstruction–WFR model is of critical importance for accurate wind estimates–What kind of shear/veer model?–How to quantify model inadequacy? (e.g. fitting residuals)• Rotor equivalent wind speed–By integration of shear profile?–Some geometrical issues…• Accounting for terrain–Elevation data integrated as inputs to the WFR codes?–Classification of terrain: different WFR models to recommend?• Practical questions–integration of brackets into turbine design–alignment of nacelle lidar to rotor axis DTU Wind Energy, Technical University of DenmarkThanks for your attention!More info: website www.unitte.dk contact:borr@dtu.dk, rozn@dtu.dk11Scientific article: Wind Energy ScienceMy PhD thesis:Remotely measuring the wind using turbine-mounted lidarsDTU Wind Energy, Technical University of DenmarkDoes this make it any easier?12Flow disturbed by turbine wakes !Perdigão.credit: N. Vasiljevic (very)complexterrainDTU Wind Energy, Technical University of DenmarkPreparing for questions-Wind Field Reconstruction13DTU Wind Energy, Technical University of Denmark14Measured Power curves (binned)WFR using wind-induction modelDTU Wind Energy, Technical University of Denmark15Power curve uncertainties: power, type AWFR using wind-induction model• Clear reduction of scatter in power curve nacelle lidars yield smaller type A (statistical) power uncertaintyDTU Wind Energy, Technical University of Denmark16Power curve uncertainties: combinedWFR using wind-induction model• Results are mostly dependent on type B wind speed uncertainty very sensitive to the ”terrain uncertainty” lidar uncertainties are smaller only due to this component…DTU Wind Energy, Technical University of Denmark17Annual Energy production• Derived as percentage of AEP using ”mast power curve”• 3 methods:– Wind model– Combined wind-induction• Wind speed estimated at 2.5D• fitted free stream wind speed (𝑉𝑉∞) ZDM5B-demoDTU Wind Energy, Technical University of DenmarkFull-scale campaign: Nørrekær Enge• in Jutland, Denmark• owner: Vattenfall• 13 Siemens turbines of 2.3MW18DTU Wind Energy, Technical University of DenmarkWind speed results: summary table19• Overestimation of 1-1.5% with the wind model• Better performance of wind-induction model using the lidars’ short-range measurements• Lidar-to-lidar: 5B-Demo about 0.5-1% higher than ZDMDTU Wind Energy, Technical University of DenmarkWind speed results: summary table20• Disjoint datasets: similar observations• Increased number of valid data points (2-3x more)• R2 enhanced slightlyDTU Wind Energy, Technical University of DenmarkWind speed results: summary table21• Better agreement between lidar and mast• Much larger scatter (“signal decorrelation”)• Still 5B-Demo above ZDM (about 0.5%)DTU Wind Energy, Technical University of DenmarkWind speed results: summary table22DTU Wind Energy, Technical University of DenmarkYaw misalignment results:WFR using the wind-induction model23• Wind sector: [110°, 219°] (joint datasets)• “Ref.” yaw misalignment from spinner anemometerHigher scatter with lidars than spinner “mean” yaw misalignment: ≈ −3°The two nacelle lidars seem to provide similar results5B-demo: 4 dist,from 0.5 to @1.2D_rotZDM: 3 dist.From 0.3 to 1.2D_rotSpinner anemometerDTU Wind Energy, Technical University of DenmarkShear exponent results:WFR using the wind-induction model24• Wind sector : [110°, 219°] (joint datasets)• “Ref.” shear exponent: from mast, using cups at 80 and 57m aglSlight overestimation vs. mast  Similar results between the two lidars5B-demo: 4 dist,from 0.5 to @1.2D_rot𝒚𝒚 = 𝟎𝟎.𝟗𝟗𝟗𝟗𝟗𝟗𝟗𝟗𝒆𝒆 + 𝟎𝟎.𝟎𝟎𝟎𝟎𝟗𝟗𝟗𝟗 𝑹𝑹𝟐𝟐 = 𝟎𝟎.𝟔𝟔𝟔𝟔𝟔𝟔𝟎𝟎𝒚𝒚 = 𝟎𝟎.𝟎𝟎𝟗𝟗𝟎𝟎𝟎𝟎𝒆𝒆 𝑹𝑹𝟐𝟐 = 𝟎𝟎.𝟔𝟔𝟔𝟔𝟎𝟎𝟎𝟎ZDM: 3 dist.From 0.3 to 1.2D_rot𝒚𝒚 = 𝟎𝟎.𝟗𝟗𝟗𝟗𝟎𝟎𝟎𝟎𝒆𝒆 + 𝟎𝟎.𝟎𝟎𝟗𝟗𝟗𝟗𝟐𝟐 𝑹𝑹𝟐𝟐 = 𝟎𝟎.𝟔𝟔𝟔𝟔𝟔𝟔𝟎𝟎𝒚𝒚 = 𝟎𝟎.𝟎𝟎𝟗𝟗𝟗𝟗𝟐𝟐𝒆𝒆 𝑹𝑹𝟐𝟐 = 𝟎𝟎.𝟔𝟔𝟓𝟓𝟓𝟓𝟗𝟗DTU Wind Energy, Technical University of DenmarkInduction factor results:WFR using the wind-induction model25• Wind sector : [110°, 219°] (joint datasets)• “Ref.” induction factor: 𝐶𝐶𝑇𝑇 from “HAWC2” simu, 𝑎𝑎 = 0.5 ⋅ 1 − 1 − 𝐶𝐶𝑇𝑇5B-demo: 4 dist,from 0.5 to @1.2D_rotZDM: 3 dist.From 0.3 to 1.2D_rotDTU Wind Energy, Technical University of DenmarkLOS velocity fitting residuals• Definitions:– 𝑽𝑽𝐥𝐥𝐥𝐥𝐥𝐥 and �𝑽𝑽𝐥𝐥𝐥𝐥𝐥𝐥 are column vectors of length = N meas. points (e.g. 5B-Demo = 4 dist*5 los =20; ZDM = 3 dist*6 los =18)–“bias” = 𝑽𝑽𝐥𝐥𝐥𝐥𝐥𝐥 − �𝑽𝑽𝐥𝐥𝐥𝐥𝐥𝐥 ; “error”: = 𝒂𝒂𝒂𝒂𝒂𝒂 𝑽𝑽𝐥𝐥𝐥𝐥𝐥𝐥 − �𝑽𝑽𝐥𝐥𝐥𝐥𝐥𝐥26observationspredictionsDTU Wind Energy, Technical University of DenmarkLOS velocity fitting residuals• Computed stats: –M:mean, N:normalised; F:fractional; –S: squared; R: root; SS: sum of squares–MB, ME, NMB, NME, MFB, MFE, SSE, MSE, RMSE, NMSE27observationspredictionsDTU Wind Energy, Technical University of DenmarkV_los fitting residuals: mean biasWFR using the wind-induction model28• Wind sector : [110°, 219°] (joint datasets)MB show very low values;Histogram centered on zero: the used model is “unbiased”5B-demo4 dist. from 0.5 to @1.2D_rotZDM3 dist. from 0.3 to 1.2D_rot𝟎𝟎𝟎𝟎−𝟗𝟗𝟎𝟎𝟎𝟎−𝟗𝟗DTU Wind Energy, Technical University of Denmark• Wind sector : [110°, 219°] (joint datasets)RMSE values between 0 and 0.25 m/sSimilar distributions for both lidars, with a slightly larger mean for ZDMV_los fitting residuals: mean biasWFR using the wind-induction model295B-demo4 dist. from 0.5 to @1.2D_rotZDM3 dist. from 0.3 to 1.2D_rotDTU Wind Energy, Technical University of DenmarkA simple induction model• Derived from the Biot-Savart law–see The upstream flow of a wind turbine: blockage effect–two parameters: induction factor 𝑎𝑎, free wind speed 𝑈𝑈∞𝑈𝑈𝑈𝑈∞= 1 − 𝑎𝑎 1 + 𝜉𝜉1+𝜉𝜉2, with 𝜉𝜉 = 𝑥𝑥𝑊𝑊𝑅𝑅𝑟𝑟𝑟𝑟𝑟𝑟30ZDM5B-demoDTU Wind Energy, Technical University of DenmarkSimple induction models• One- or two- dimensional?31

Nacelle lidar for power perf. – the UniTTe approach to retrieve V∞

http://orbit.dtu.dk/ws/files/164866300/2017_09_27_ABorraccino_IEA_wind32_naclidar_PCV_UniTTe.pdf

Nacelle lidar for power perf. – the UniTTe approach to retrieve V∞

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