turbulent wind seed selection using robust statistics for design load

PO.ID
033
TURBULENT WIND SEED SELECTION USING ROBUST STATISTICS
FOR DESIGN LOAD ANALYSIS OF WIND BLADE DESIGN
Soo-Hyun KIM*a, Hyungki SHINa, Hyung-Joon BANGa, Moon-Seok JANGa, Young-Chul JOOa
aKorea Institute of Energy Research , Rep. of KOREA (*[email protected])
Abstracts
WT Model and Simulation Methods
Wind energy is one of the fastest growing renewable
energy technologies. Due to meet that demand, the
commercial wind turbines have been developed
consistently in size over the years. Because of the huge
size and severe external loading conditions, the load
calculation of wind turbine system should be effective as
well as conservative. However an excessively
conservative load in blade design would lead to an oversized rotor design and consequently result a higher cost
on the entire systems. Therefore an appropriate method
for design load calculation of wind turbine has to be
selected carefully.
In this paper, a turbulent wind seed selection method is
analyzed using robust statistics, which provides an
alternative approach to standard statistical methods.
Design load simulations of turbulent wind were performed
for two different wind turbine system; 2MW and 7MW class
models. Based on the robust z-score calculated with
design load simulation results of 15 wind fields generated
with different random seeds, the three turbulent wind
seeds were chosen for two system models. The results of
the classical and statistical methods were compared and
investigated.
Introduction
The IEC 61400-1 standard provides the stochastic
turbulence wind models to be generated with the specified
mean value (mean wind speed at hub height) and the
standard deviation (turbulence intensity). Because of its
statistical characteristics, the load simulations using the
turbulent wind model should be performed with different
initial values (“seeds”) for producing the turbulent wind
field. As the difference in seed values of turbulent wind
model could lead to considerable variations in the results,
the seeds were selected for turbulent wind generation
prior to wind turbine analyses.
There have been some studies about the influences of
wind seed and wind turbine loads. Anand Natarajan and
David R. Verelst performed turbulent wind load simulations
with FAST and HAWC2 using 20 random wind seeds
between 5 and 25 m/s of mean wind speeds. Each load
simulation was performed with independently generated
10 min turbulent wind input. It was found that there is a
band of variation for each mean wind speed and for the 20
seeds chosen for the study; the results show consistent
spreads in the loads at each mean wind speed.
•
-
-
Design 2MW & 7MW Class Wind Turbine Blade
Load analysis for 50+m and 70+m length blade design
Generic multi-MW class wind turbine system model used
(w/ 3-blade, up-wind, pitch regulated, geared, variable
speed type)
DLCs for extreme load calculation selected according to
IEC 61400-1 3rd ed. and/or GL guideline ed. 2010
-
• Robust Statistics
- Alternative approach to
classical methods
- Using median(M),
normalized interquartile
range (N.IQR)
- Less affected by outliers
or other small departures
from model assumptions
10m/s
Wind
seed
ID
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Mean
Median
Blade1 Mxy @root
Result Classical Robust
Data
Z-score Z-score
Result
Data
Hub Myz
Classical Robust
Z-score Z-score
Tower Mxy @bottom
Result Classical Robust
Data
Z-score Z-score
Seed selection
Classical
Robust
Z-score
Z-score
min
min
min
max
max
max
max
max
max
sum
rank
sum
rank
0.991
0.943
1.022
1.095
1.029
0.927
1.004
0.931
1.037
1.035
0.914
1.013
0.979
1.034
1.045
1.000
1.013
0.18
1.11
0.43
1.84
0.57
1.40
0.07
1.33
0.72
0.68
1.65
0.25
0.40
0.66
0.87
0.40
1.28
0.17
1.50
0.30
1.56
0.17
1.50
0.45
0.41
1.80
0.00
0.62
0.38
0.59
0.985
0.979
1.127
1.068
1.079
1.024
1.093
0.963
0.999
0.967
0.940
0.930
0.954
0.948
0.943
1.000
0.979
0.24
0.34
2.02
1.08
1.25
0.38
1.47
0.59
0.01
0.52
0.95
1.11
0.73
0.82
0.90
0.09
0.00
2.11
1.27
1.43
0.64
1.62
0.22
0.29
0.16
0.55
0.70
0.35
0.43
0.50
0.946
0.881
1.083
0.973
0.992
1.021
0.910
1.061
1.074
1.039
0.917
1.055
1.096
0.963
0.989
1.000
0.992
0.79
1.75
1.22
0.40
0.12
0.31
1.32
0.89
1.09
0.58
1.22
0.81
1.42
0.54
0.16
0.59
1.45
1.19
0.25
0.00
0.38
1.06
0.90
1.08
0.62
0.97
0.83
1.37
0.37
0.03
0.40
1.07
1.22
1.11
0.65
0.70
0.95
0.94
0.61
0.59
1.27
0.72
0.85
0.67
0.64
1
12
14
13
5
7
11
10
3
2
15
8
9
6
4
0.36
0.91
1.16
1.01
0.58
0.86
0.95
0.87
0.61
0.40
1.11
0.51
0.78
0.40
0.37
1
11
15
13
6
9
12
10
7
4
14
5
8
3
2
• Design Load Analysis
- 15 wind fields generated by GH Bladed v4 with same
characteristics & different random seeds
- For 10m/s(about rated), 25m/s(cut-out) and 42.5m/s
(extreme wind speed w/ recurrence period of 50yr)
- Loads calculated for the bending moments of blade
root, hub and tower bottom, and the extreme peak
values extracted for each wind speed and seeds
• Turbulent Wind Seed Selection
- Rank the peak load value of 15 wind seeds w.r.t
classical & robust z-score index
- Compare the results between 15 seeds
- Compare the results between 2 statistics methods
Variation of design loads w.r.t. wind seed (7MW WT @ 10m/s)
Analysis Results
• Load Simulation Results of 2MW model
10m/s
Wind
seed
ID
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Mean
Median
Blade1 Mxy @root
Result Classical Robust
Data
Z-score Z-score
Result
Data
Hub Myz
Classical Robust
Z-score Z-score
Tower Mxy @bottom
Result Classical Robust
Data
Z-score Z-score
Seed selection
Classical
Robust
Z-score
Z-score
min
min
min
max
max
max
max
max
max
sum
rank
sum
rank
1.025
0.954
1.076
1.025
1.013
1.028
0.976
1.017
0.960
0.963
1.007
0.970
0.978
0.950
1.057
1.000
1.007
0.63
1.19
1.97
0.64
0.35
0.72
0.61
0.42
1.03
0.94
0.19
0.76
0.57
1.28
1.47
0.40
1.26
1.61
0.40
0.14
0.48
0.73
0.21
1.11
1.03
0.00
0.87
0.69
1.33
1.16
0.920
0.959
1.003
1.131
1.006
1.138
0.941
0.836
0.825
1.025
1.052
1.220
0.926
1.041
0.978
1.000
1.003
0.74
0.38
0.03
1.21
0.05
1.27
0.55
1.52
1.62
0.23
0.48
2.03
0.68
0.38
0.20
0.99
0.53
0.00
1.53
0.03
1.61
0.74
2.00
2.13
0.26
0.58
2.59
0.92
0.45
0.29
1.014
0.988
1.012
1.038
1.011
1.020
0.998
0.980
0.970
1.014
0.991
1.022
0.964
1.005
0.974
1.000
1.005
0.63
0.54
0.56
1.76
0.53
0.94
0.11
0.94
1.40
0.64
0.40
1.00
1.67
0.21
1.21
0.41
0.74
0.34
1.52
0.31
0.72
0.32
1.14
1.58
0.42
0.60
0.77
1.85
0.00
1.39
0.67
0.70
0.85
1.20
0.31
0.98
0.42
0.96
1.35
0.60
0.36
1.26
0.97
0.62
0.96
6
7
8
13
1
12
3
10
15
4
2
14
11
5
9
0.60
0.84
0.65
1.15
0.16
0.93
0.60
1.12
1.61
0.57
0.39
1.41
1.15
0.60
0.95
6
8
7
12
1
9
5
11
15
3
2
14
13
4
10
Variation of design loads w.r.t. wind seed (7MW WT @ 25m/s)
Conclusions
Load simulation results w.r.t. wind seed (2MW WT @ 10m/s)
Robust statistics provides an alternative approach to
standard statistical methods, which are not unduly affected
by outliers or other small departures from model
assumptions.
Classical Statistics
Assumed data normally
distributed
Using mean() and
standard deviation()
When „outlier‟ exist in data,
classical Z-scores often
have very poor
performance
• Load Simulation Results of 7MW model
Load simulation results w.r.t. wind seed (7MW WT @ 25m/s)
Robust Statistics Analysis
•
-
Analysis Results
•
-
Blade Design Load Analysis
Design load analysis for multi-MW class wind turbines
Extreme peak value of bending moment of major
location extracted for each wind speed and seeds
•
-
Compare Load Results in Different Wind Seeds
Extreme load variation up to 5~40%, show larger
difference at the case of higher wind speed
Rank of each seed according to seed selection are not
same between results of 2MW and 7MW model
Proper turbulent wind seed should be selected before
design load calculation process for each WT case
-
Variation of design loads w.r.t. wind seed (2MW WT @ 10m/s)
•
-
Compare Classical & Robust Statistics Methods
Due to the influence by outlier, some difference results
shown between two seed selection methods
Robust Z-score suggest more proper representative
value than classical methods
References
•
1. Paul S. Veers, etc, Trend in the design, manufacture and evaluation of wind
turbine blades, Wind Energy, 2003, vol. 6, pp. 245-259.
2. Anand Natarajan and David R. Verelst, Outlier robustness for wind turbine
extrapolated extreme loads, Wind Energy, 2012, vol. 15, pp. 679–697.
3. IEC 61400-1, Wind Turbine - Part 1: Design requirements, 3rd ed., 2005-08.
4. Germanischer Lloyd, Rules and Regulation IV - Guideline for the Certification
of Wind Turbines, Edition 2010.
Z-score
- Performance indicator how much the reported result differs
from the assigned value
|Z|  2 : acceptable
2  |Z|  3 : questionable
3  |Z|
: „outlier‟
Variation of design loads w.r.t. wind seed (2MW WT @ 42.5m/s)
This work was supported by the Korea Institute of Energy Technology Evaluation
and Planning(KETEP) granted financial resource from the Ministry of Trade,
Industry & Energy, Republic of Korea (No. 20123010020070 &
20123010020130)
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