FARMOPT: WIND TURBINE WAKE MEASUREMENT IN COMPLEX TERRAIN
Kurt S. Hansen et al.
Mail: [email protected]
DTU Wind Energy, Technical University of Denmark
Outline
• Objectives for FarmOpt;
• Data overview, site description, windfarm layout and measurement setup;
• Tower bending;
– Calibration;
– Thrust coefficient validation;
– Fatigue loads;
• SCADA data qualification;
• 360 deg power polars;
– Power deficits;
– Discussion of terrain & wake effects;
• Single near wake analysis
– Wake identifications including visualizations;
• Summary;
• Acknowledgements;
• Perdigao wake measurements
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DTU Wind Energy, Technical University of Denmark
Objectives FarmOpt
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• Development of wind farm optimization tools for optimally placing wind turbines in wind farms located in complex terrain.
• Requirement: full scale measurements from wind farms in complex terrain for software verification.
• The focus are the SCADA data, wind and load measurements obtained from a wind farm - located in complex terrain.
DTU Wind Energy, Technical University of Denmark
Field measurements from the wind farm – located in complex terrain
• SCADA data from 25 wind turbines (12 months);
– Difficult to qualify the data;
– Icing problems during winter;
– Determination of ambient inflow conditions;
• 10-minute statistics from 2 x 70 m masts (12 months);
– Cup, vanes, temp & pressure;
• High simpled data (~7 months);
– 3-D sonic data
Vertical wind speed, turbulence;
Atmospheric stability;
– Tower bending (Thrust)Lack of calibration
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DTU Wind Energy, Technical University of Denmark
Location of Shaanxi wind farm, close to Jingbian (close to Inner Mongolia)
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Shaanxi WF
~300 km N of Xi’an
~700 km SE of Beijing
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DTU Wind Energy, Technical University of Denmark
25 wind turbines - in complex terrain
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DTU Wind Energy, Technical University of Denmark
Inflow from N is not complex – but!
Photos from a site visit
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SE
S
SW
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Wind turbines
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Type: 25 x H93/CSIC
Rated power: 2.0 MW
Diameter: 93 m
Hub height: 67 m
Control: VS & VP
Info: Power & CT curves
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Tower bending
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Preferred Theoretical Backup Iterative
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Calibration of CT-coefficient
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Thrust for westerly inflow (slope sector)
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DTU Wind Energy, Technical University of Denmark
Tower bending
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Free inflow
Free inflow
Slope inflow
Slope inflow
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Wind farm analysis – SCADA data
• Data qualification (filtering)
– Start/stop
– Parked/idling
– Curtailment
– Calibration (yaw positions)
– Calculate equivalent rotor wind speed (PC & PA vs U)
• Park efficiency depends on
– Inflow conditions (speed, direction)
– Terrain complexity
– Local wake effects
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DTU Wind Energy, Technical University of Denmark
Inflow conditions (speed,direction)
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• Difficult to establish robust inflow conditions;
• “Disturbing” turbines next to the wind farm;
• Yaw position should be calibrated before use;
• Assumption about the power curve validity.
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Benchmarks
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Benchmarks
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Wind farm layout, spacing 4 - 8 Diameters
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Maximum wake deficit, for 4, 5, 6 & 7D spacing.
Other pairs with viz. wake deficit.
DTU Wind Energy, Technical University of Denmark
Definition of power deficit = 1 – power ratio = 1 – Pwake/Pfree
Wake deficit distribution in-side the wind farm, winddir: W => E;
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Maximum deficit
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wt10/wt08
wt25/wt21
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Discussion of wind farm park effects.
• Western inflow:
– Narrow sectors are characterized with low power variability;
– Wake deficit distributions between pairs of single turbines can beidentified;
– Wake effects can be identified in the power polars;
• Eastern inflow:
– No distinct wake deficit between turbines can be identified;
– The large variability in the power signals seems to be causedby terrain effects;
– More scada is needed before a firm conclusion can be made.
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Single wind turbine wake analysis (wt14)
DTU Wind Energy, Technical University of Denmark
Single wind turbine wake analysis (wt14)
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SN
2.2D 1.4 D
M1
M3
wt14
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SWSE
Spacing 2.2D
Wake analysis (wt14), WD=SE-S-SW
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Turbulence in wake (wt14)
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Measured on mast
Ti calculated with reference to ambient wind speed
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NW NE
Spacing 1.4D
Wake analysis (wt14), WD=NW-N-NE
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Deficit & turbulence in double wakes
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TI
TIM1
M3
wt12 wt14
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Deficit in double wakes
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~5D spacing
~1.4D spacing
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Turbulence in double wakes
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~5D spacing
~1.4D spacing
DTU Wind Energy, Technical University of Denmark
Summary
• A database with 10-minute measurements representing 1 YR has been established.
– Wind speed @ 3 different levels on 2 masts
– Wind direction, temperatures and atmospheric pressure.
• A database with 1 minute and 10-minute statistics of SCADA data for 25 wind turbines representing 1 YR of operation has been established.
– Active power, Pitch, Rotor speed, Yaw position and Nacelle wind speed
– Rotor equivalent wind speed
• A database with 10-minute statistics for 35 Hz time series representing ½ YR has been established.
– Signals and derived signal representing 3 x 3D sonics
– Tower bending TBx, Tby, TBF-A and Thrust.
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DTU Wind Energy, Technical University of Denmark
Literature
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CFD Simulations of Flows in a Wind Farm in Complex Terrain and Comparisons to Measurements
Sessarego M, Shen WZ, Maarten PVL, Hansen KS, Zhu WJ.
Appl. Sci. 2018, 8(5), 788; https://doi.org/10.3390/app8050788
DTU Wind Energy, Technical University of Denmark
Acknowledgements
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Co-authors
Gunner C. Larsen1, Ju Feng1, Andrea Vignaroli1, Wei Jun Zhu1, Wei Liu2, Chang Xu3 and Wen Z. Shen1,1 Department of Wind Energy, Technical University of Denmark2 North West Survey and Design Institute Hydro China Consultant Corporation (NWI), Xi’an, China3 Hohai University, Nanjing, China
Farmopt was funded by the Energy Technology Development and Demonstration Program in 2013 (EUDP).
DTU Wind Energy, Technical University of Denmark
Perdigão experiment
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2 MW Wind turbine
Location of 40m mast Jan 2002–Dec 2004
DTU Wind Energy, Technical University of Denmark
Objectives of the Perdigão experiment
To perform an experimental investigation of the flow over a double ridge using two sets of synchronized LiDAR systems (SR & LR windscanners).
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High quality field data for model validation is obtained for use to investigate:
I. Wind resources in complex terrain and hazardous events (NEWA project)
II. Inflow conditions for wind turbines in complex terrain (UniTTe & FarmOpt projects)
III.Wind turbine wakes in complex terrain (FarmOptproject)
DTU Wind Energy, Technical University of Denmark
Perdigão site: wind turbine located on a ridge.
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Summit heights 500-550 m
Terrain flats out towards SW and NE
Terrain coverage irregular (forest patches of eucalyptus and pine trees)
SW NE
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Diamond scan for a horizontal, inclined plane, (obtained by 2 x long-range (LR) windscanners)
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LR Windscanner examples
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LONGwake
SHORT wake
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Vertical near wake scanning at 1D spacing, (obtained with 3 x short-range (SR) wind scanners)
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WakeDOWN
WakeUP
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Diurnal cycle analysis of identified wake cases.
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Example of wake speed ratio distributions
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Summary of the single wake analysis
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• No overlapping periods for the SR & LR windscanners.
• Limited number of interesting wake periods due to the narrow inflow sector (10⁰);
• The wake behaviour seems to correlate with the vertical wind speed.
• The stability effects seems to determine the wake characteristics (eg. extension, position, dissipation);
DTU Wind Energy, Technical University of Denmark
Conclusion
• Near wake behaviour can be derived from windscanner measurements;
• The vertical position of the near wake seems to move:
– Down-hill during nighttime (summer);
– Up-hill during daytime (summer);
• The terrain complexity combined with the ambient turbulence, determines the how the wind turbine wake dissipates.
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DTU Wind Energy, Technical University of Denmark
Literature
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Does the wind turbine wake follow the topography?
A multi-lidar study in complex terrain.
Menke R,Vasiljevic N,Hansen KS, Hahmann AH, Mann J
Wind Energ. Sci., 3, 681–691, 2018
https://doi.org/10.5194/wes-3-681-2018
DTU Wind Energy, Technical University of Denmark
Acknowledgements - Perdigão experiment
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Co-authors
Gunner C. Larsen1, Robert Menke1, Nikola Vasiljevicv, Nikolas Angelou1
1 Department of Wind Energy, Technical University of Denmark
Farmopt was funded by the Energy Technology Development and Demonstration Program in 2013 (EUDP), UniTTe was supported by The Danish Council for Strategic Research (DSF) in 2013 and the New European Wind Atlas (NEWA) has been supported by the EUROPEAN COMMISSION.