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On the impact of the assimilation of nacelle winds and yaw angles with WRF-FDDA andWRF-DART for short-term wind energy predictions
Draxl, Caroline; delle Monache, Luca; Liu, Yubao; Cheng, William; Descombes, Gael; Vandenberghe,Francois; Romine, Glen; Knievel, Jason; Hahmann, Andrea N.; Giebel, Gregor
Publication date:2011
Document VersionPublisher's PDF, also known as Version of record
Link back to DTU Orbit
Citation (APA):Draxl, C., delle Monache, L., Liu, Y., Cheng, W., Descombes, G., Vandenberghe, F., ... Giebel, G. (2011). Onthe impact of the assimilation of nacelle winds and yaw angles with WRF-FDDA and WRF-DART for short-termwind energy predictions [Sound/Visual production (digital)]. 14th Conference on Mesoscale Processes/15thConference on Aviation, Range, and Aerospace Meteorology, Los Angeles (US), 1-4 Aug, 01/01/2011,http://ams.confex.com/ams/14Meso15ARAM/flvgateway.cgi/id/18490?recordingid=18490
Risø DTU NCAR
...a bit about everything...
Caroline Draxl
Caroline Draxl
Luca delle Monache, Yubao Liu, William Cheng, Gael Descombes, Luca delle Monache, Yubao Liu, William Cheng, Gael Descombes, FranFranççois Vandenberghe, Glen Romine, Jason Knievel (NCAR)ois Vandenberghe, Glen Romine, Jason Knievel (NCAR)Andrea N. Hahmann, Gregor Giebel (Risø DTU)Andrea N. Hahmann, Gregor Giebel (Risø DTU)
Assimilation of nacelle winds and yaw angles for Short-Term Wind Energy Predictions
Risø DTU National Laboratory for Sustainable Energy
Forecast errors are expensive
Trading of wind power on energy markets
Fluctuating wind and power is challenging for grid operators
Optimisation of power plant mix
Maintainance planning
Enhanced value and acceptance of wind power
Wind turbine measurements become increasingly available and constitute a new set of measurements in the PBL
How to treat them in data assimilation?
What is their benefit in the system??
Nacelle winds and yaw angles
Nacelle winds and yaw angles areused by the turbine control system for optimal turbine operation.
Nacelle winds:
Sonic anemometers measure wind on Horns Rev turbines @ 70 m.
Yaw Angles for wind directions(u and v in data assimilation):
The nacelle turns itself into the wind.
Issues with nacelle winds
Disturbance depends on
Design of turbine and nacelle
Pitch/stall regulation
Height of anemometer and position on nacelle
Operation/standstill
Position in wind farm: wake
Nacelle transfer function
Restricted data access
Wind Farm layout and model setup
30 km
10 km3.3 km
Horns Rev Wind Farm Measurement mast
Denmark10 km
Assumption of spatially independentobs errors in data assimilation violated
=> data thinningWRF FDDA V3.2.1
Find optimal case studies
→ 4 days in 4 different months in 2005:
54 hours with a 6h-forecast every hour 54 forecasts→
low pressure over northern Europe, pre or post cold front
WIND70 m tower280°
260°wake
6 km
Data thinning
Statistics compared for different turbine groups
Medians of
all turbines
WIND70 m tower280°
260°wake
Data thinning
Medians of
all turbines
upwind half
first 2 upwind turbine rows
WIND70 m tower280°
260°wake
Data thinning
Medians of
all turbines
upwind half
first 2 upwind turbine rows
first upwind turbine row
Direction measurements from turbine yaws
WIND70 m tower280°
260°wake
Data thinning
Medians of
all turbines
upwind half
first 2 upwind turbine rows
first upwind turbine row
Direction measurements from turbine yaws
and from 68 m tower to the north of the farm
WIND70 m tower280°
260°wake
Data thinning
Medians of
all turbines
upwind half
first 2 upwind turbine rows
first upwind turbine row
Direction measurements from turbine yaws
and from 68 m tower to the north of the farm
+ METAR, SYNOP, ACARS, Radiosondes, Ships
WIND280°
260°70 m towerwake
1h 2h 3h 4h 5h 6h Lead time
BIA
S (
model
– o
bs)
m/s
0.7
5 0.8
0.8
5 0.9
0
.95 1
1.0
5 1.1
1
.15
Bias reduced in first 2 hours, noisy
no FDDA
Bias reduced in first 2 hours, noisy
1h 2h 3h 4h 5h 6h Lead time
BIA
S (
model
– o
bs)
m/s
0.7
5 0.8
0.8
5 0.9
0
.95 1
1
.05 1.1
1
.15
All + MADIShalf + MADIS
no FDDAMADIS only
Bias reduced in first 2 hours, noisy
1h 2h 3h 4h 5h 6h Lead time
BIA
S (
model
– o
bs)
m/s
0.7
5 0.8
0.8
5 0.9
0
.95
1 1
.05 1.1
1
.15
All + MADIShalf + MADISrow 1 + MADISRows 1&2 + MADISRows 1&2 + tower + MADISno FDDAMADIS only
Bias reduced in first 2 hours, noisy
1h 2h 3h 4h 5h 6h Lead time
1h 2h 3h 4h 5h 6h Lead time
BIA
S (
model
– o
bs)
m/s
0.7
5 0.8
0.8
5 0.9
0
.95
1 1
.05 1.1
1
.15
R
MSE m
/s1.4
1
.5 1.6
1.7
1
. 8 1
.9 2
2.1
2.2
2.3
All + MADIShalf + MADISrow 1 + MADISRows 1&2 + MADISRows 1&2 + tower + MADISno FDDAMADIS only
Bias reduced in first 2 hours, noisy
1h 2h 3h 4h 5h 6h Lead time
1h 2h 3h 4h 5h 6h Lead time
BIA
S (
model
– o
bs)
m/s
0.7
5 0.8
0.8
5 0.9
0
.95
1 1
.05 1.1
1
.15
R
MSE m
/s1.4
1
.5 1.6
1.7
1
. 8 1
.9 2
2.1
2.2
2.3
All + MADIShalf + MADISrow 1 + MADISRows 1&2 + MADISRows 1&2 + tower + MADISno FDDAMADIS only
Outliers Weight too high ?
WRF DART and plans
DART: Community Ensemble Kalman filter system maintained by NCAR
EnKF: flow-dependent background error covariances.
Observation error needs to be assigned to measurements – knowledgeof measurements is important.Observation errors are used by EnKF as a weight for the information provided by the measurements.
Source: Tom Hamill, http://www.esrl.noaa.gov/psd/people/tom.hamill
A new growing & promising data set
CAREFULwith nacelle winds !!!
Thinning strategies
FDDA: 2 hour benefit
Improve forecasts inland
WRF DARTMore promising ?
?
Assimilation in the PBL
A new growing & promising data set
THANK YOU [email protected]
CAREFULwith nacelle winds !!!
Thinning strategies
FDDA: 2 hour benefit
Improveforecasts inland
WRF DARTMore promising ?
?
Assimilation in the PBL