Lidar Measurements at SWIFT

Transcription

1 Photos placed in horizontal position with even amount of white space between photos and header Lidar Measurements at SWIFT David Maniaci and Thomas Herges IEA Task Meeting October 4, 2016 Sandia National Laboratories is a multi-mission laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the U.S. Department of Energy s National Nuclear Security Administration under contract DE-AC04-94AL SAND NO XXXXP SAND PE

2 SWiFT Site Layout and Capabilities SWiFT exists to: DOE/SNL Scaled Wind Farm Technology (SWiFT) facility hosted by Texas Tech University (TTU) Reduce turbine-turbine interaction and wind plant underperformance Public, open-source validation data Advance wind turbine technology Facilities: Three variable-speed variable-pitch modified wind turbines with full power conversion and extensive sensor suite Two heavily instrumented inflow anemometer towers Site-wide time-synchronized data collection

3 V&V Framework Integrated Planning Program leaders, modelers, software developers, experimentalists, V&V specialists Application: Specify system scenario and response quantities (SRQ) to be predicted at plant scale Phenomena Identification: Identify and prioritize the plant scale phenomena required for models to successfully predict the SRQ for system scenario P Integrated Program Planning Validation Hierarchy: Identify and prioritize those phenomena for which the models should be tested, the scales and hierarchy required for the tests, and conceptually how the validation tests should occur Prioritize experiments within hierarchy based on program needs and resources Document Validation Planning Domain specific program leaders, modelers, experimentalists, V&V specialists, data acquisition specialists Experiment Design, Execution & Analysis through tightly coupled experimental/modeling effort Document Solution Verification: Mesh convergence error Validation Metrics Code Verification: Software and algorithm quality assessment Assessment Integrated Experiment and Model Planning and Execution Credibility of processes used Document

4 Wind Plant Scale Phenomenon Importance Ranking Table (PIRT) P henomenon Inflow T urbulence/w ake Interaction P hysics Code V al W ind direction (shear/veer/asymetry) H L M M Turbulence characteristics (intensity, spectra, coherence, stability) Importance at Application Level M odel Adequacy W ake sensitivity to gusts, low-level jets, dimensional boundary layers H L M M W ake sensitivity to turbulence Issue/Comment Coherent turblence structure H L M L W ake sensitivity to organized structures Surface conditions (roughness, canopy, waves, surface heat flux, topography) Momentum transport (horizontal and vertical fluxes) M ulti-t urbine W ake E ffects W ake interaction, merging, meander Plant flow control for optimum performance W ake steering (yaw & tilt effects) W ake dissipation W ake Impingement (full, half, etc.) Deep array effects (change in turbulence, etc.) O ther E ffects W ind plant blockage effects and plant wake Acoustic Propagation H L M M W ake sensitivity to changes in ABL due H L L L W ake sensitivity to momentum supplied a special case, as well as the deep array H L L L Physics behind unsteady wake behavio H M M L H L L L Strategies for optimizing the wind farm turbines Effect of non-normal inflow on wake beh control. H L L L Evolution toward a neglibly small wake H L L L Effect of upstream wake position on do H L L L Emphasis on the behavior of the flow w M M M L Emphasis on the effect of the wind farm downstream regions H L L L Noise generation and propogation throu

5 Wind Plant Validation Hierarchy Industrial Scale Wind Plant System Scaled Wind Farm in Wind Tunnel Scaled Wind Farm In Field Subsystem Wake/Turbine Interaction in Wind Tunnel Integrated Effects (Benchmark) Single Wind Turbine Hierarchy Wake Steer/Veer Multiple Wakes with Inflow Turbulence Infinite Wind Farm Wind Tunnel Separate Effects (Unit Problems)

6 Multi-Turbine Wake Effects Interaction, Merging, Motion Steering (Yaw/Tilt Effects) Wake Dissipation Wake Impingement (Full/Half/Near) Inflow Turbulence Wake Interaction Wind Direction Surface Conditions Turbulence Statistics Momentum Transport Acoustic Propagation Testing Issues Boundary Conditions PPEM (Prioritized Phenomenon Experiment Mapping) Wind Plant Physics Present/Physics Measured Entirely Mostly Somewhat Limited Missing Industrial Scale Wind Farm in (60 m rotor) Physics Present Physics Capture by Measurements Scaled Wind Farm in Field (20 m rotor) Physics Present Physics Capture by Measurements Scaled Wind Farm in VL WT (2 m rotor) Physics Present Physics Capture by Measurements Wake/Turbine Interaction in WT (2 m rotor) Physics Present 6

7 Prevailing Wind SWiFT Wake Steering Calibration Goal: Calibrate wake steering model (FLORIS) using single turbine wake measurements. SWiFT Wake Steering Demonstration Goal: Demonstrate the influence of wake steering control on downstream rotor power and loads using two instrumented turbines with inter-turbine wake measurements. Measurements: ABL Conditions: 200m MET, Sodar, and Radar Profiler Inflow: Dual 58.5m MET towers Rotor and Tower Strains and Accels. Wake Flow Diagnostic: DTU Windscanner 200m MET Tower SWiFT Turbines 58.5m MET Towers

8 Wake Instrumentation Identification Measure Quantities of Interest Cost Scheduling 8

9 Simulate Instrumentation SOWFA Simulated Velocity Simulated Lidar Measurements Comparison of identical time steps in order to show effect of Spinner Lidar on measurements and how that impacts wake position determination Measurements at 3D downstream of turbine 9

10 Initial Wake Measurements

11 Spatial Measurement Uncertainty error = 2.35 m wake x-position (m) Expected Deflection at 5D yaw angle ( ) 11

12 Wake Tracking Lidar data viewed 3D (81m) downstream looking upwind Unstable BL Stable BL 12

13 Conclusions The DTU Spinner Lidar is measuring the wakes from 1-5D behind a V27 turbine at the SWiFT, with changing yaw control input angles over a range of inflow conditions. A detailed lidar model was incorporated into SOWFA, which was used to ensure that the measurements would be useful for model calibration and validation. A detailed lidar calibration process was developed, and is proving important for uncertainty quantification. Future work includes developing an uncertainty quantification process for the inflow, wind turbines, and lidar measurements. Initial data campaign results anticipated for public release in January, 2017

14 1/26/2015 Thank you

15 SWiFT Integrated Experiment Planning Regional Atmosphere Atmospheric Boundary Layer Wind Farm Flow Array Flow Wake Flow Structures