12-5-2015 challenge the future delft university of technology blade load estimations by a load...
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18-04-23
Challenge the future
DelftUniversity ofTechnology
Blade Load Estimations by a Load Database for an Implementation in
SCADA Systems
Master Thesis Presentation
Carlos Ochoa A.
TUD idnr. 4145658TU/e idnr. 0756832
October 23Th, 2012
2SET MSc – Wind Energy
CONTENTS
1. Introduction
2. Objective
3. OWEZ Data
4. Method
5. Load Comparison Between Turbines
6. Load Database Construction
7. Database Estimators Validation
8. Conclusions
Blade Load Estimations by Database for SCADA
3SET MSc – Wind Energy
Z
Y
X
1. Introduction
FT(V,u,z)
FC
FG
ΩQ(V)
MY(Ω)
• Real Wind Conditions
Blade Load Estimations by Database for SCADA
Occurrences
TurbulenceWind Speed
Different inflow parameters affect the
turbine behavior, factors as:
• Wind Speed
• Wind Shear
• Turbulence
• Atmospheric stability
• etc.
All these parameters have an impact
over the forces and moments of the
turbine.
4SET MSc – Wind EnergyBlade Load Estimations by Database for SCADA
• Real Wind Conditions
• Loads and Fatigue
The cyclic loads affects the fatigue
in the materials, this limits the
lifetime of a wind turbine.
In a wind turbine, the blades are structural
components that have the largest
provability of failure after determinate
period.
1. Introduction
5SET MSc – Wind EnergyBlade Load Estimations by Database for SCADA
• Real Wind Conditions
• Fatigue
• SCADA
Collect, monitor & storage of turbine behavior
through the Standards Signals:
• Generator rotational speed and acceleration
• Electrical power output.
• Pitch angle.
• Lateral and longitudinal tower top acceleration.
• Wind Speed and wind direction.
1. Introduction
Only the main Statistics of the selected variables are computed.
• Min, max, average & standard deviation.
6SET MSc – Wind Energy
2. Objectives
Develop a method to estimate the blade load behavior by retrieving information from a
measurement database depending on the standard signals of the wind turbine, which are usually
stored by the SCADA system.
Blade Load Estimations by Database for SCADA
How accurate are the fatigue damages and the cumulative fatigue estimations when comparing them against other load estimation methods results?
Neural NetworksRegression Techniques
7SET MSc – Wind Energy
3. OWEZ Data
High frequency measurement data (32Hz) from two turbines were obtained trough a measuring
campaign at OWEZ. 41 different signals were measured for each different turbine for several months.
Blade Load Estimations by Database for SCADA
Key Signals Measured (32Hz):
•Stain signals from the root of the
blade
• Edgewise
• Flapwise
•Other 70 signals
• Standard signals
Standard Reconstruction of SCADA data
8SET MSc – Wind Energy
4. Method
The data was classified depending on the turbine, mean winds speed and turbulence intensity.
Under each wind inflow condition different load behavior is produced. From these, Rainflow counting
matrixes and load amplitudes histograms are obtained.
Blade Load Estimations by Database for SCADA
S ite In flow C ond ition C harac te riza tion
Turb
ulen
ceIn
tens
ity
2 4 6 8 10 12 14 16 18 20 22 24
24681012141618202224262830
2 4 6 8 10 12 14 16 18 20 22 24
24681012141618202224262830
2940
0
Mean Wind Speed ms
From the load amplitude
histograms, load estimators can be
derived. The groups of estimators
are storage on a database.
To perform a load estimation, the
elements of the database can be
retrieved by the use of the SCADA
data.
Load time Series
Rainflow Counting Matrixes
Load Amplitude Histograms
Load Distribution Functions
Load Estimators
10SET MSc – Wind Energy
4. Method
Blade Load Estimations by Database for SCADA
To convert the Rainflow cycle matrixes to load histograms certain material characteristics were
assumed. The geometry of the blade root (thickness and chord) was estimated.
102 103 104 105 106 10750
100
150
200
250
300
S-N Curve for the Assumed Material
Str
ess
Lo
ad
Am
plit
ud
e
S
[MP
a]
Cycles to Failure N
Equation Red. Chi-Sqr
R^2
S = a N ^b 1359.72611 0.54569
Coefficient Value Std Error
a 433.668 9.6486
b -0.09242 0.00247
A linear Goodman diagram was obtained from the use of
the assumed blade characteristics. By its use, load cycle
histograms were obtained.
11SET MSc – Wind Energy
Histogram for the Wind Bin at 11 ms and 15 TI Case with Bins of 5 KNm .
Edgewise Distribution
0 200 400 600 800
0.001
0.01
0.1
1
10
100
Cycle Amplitude KNm
Ocurrences
NFlap
Flapwise Distribution
0 200 400 600 800
0.001
0.01
0.1
1
10
100
Cycle Amplitude KNm
Ocurrences
NFlap
From the OWEZ data, the load patterns from both turbines were compared. From all the wind
conditions, the comparison results shown a remarkable similitude between loads.
Blade Load Estimations by Database for SCADA
• Turbine 8
• Turbine 7
Histogram for the Wind Bin at 13 ms and 13 TI Case with Bins of 5 KNm .
Edgewise Distribution
0 200 400 600 800
0.01
0.1
1
10
100
Cycle Amplitude KNm
Ocurrences
NFlap
Flapwise Distribution
0 200 400 600 800
0.01
0.1
1
10
100
Cycle Amplitude KNm
Ocurrences
NFlap
Histogram for the Wind Bin at 13 ms and 13 TI Case with Bins of 5 KNm .
Edgewise Distribution
0 200 400 600 800
0.01
0.1
1
10
100
Cycle Amplitude KNm
Ocurrences
NFlap
Flapwise Distribution
0 200 400 600 800
0.01
0.1
1
10
100
Cycle Amplitude KNm
Ocurrences
NFlap
5. Load Comparison Between Turbines
12SET MSc – Wind Energy
6. Load Database Construction
Blade Load Estimations by Database for SCADA
4 8 12 16 20 24
0
100
200
300
400
500
Edgewise Mean Peak Load Variation with the Wind Speed
Wind Speed (m/s)
Mean L
oadin
g (
KN
m)
All the inflow condition measured were
processed to obtain the load database.
Interesting patterns came up when analyzing
the changes of the load behavior trough
the wind speed. Especially in the
edgewise direction.
13SET MSc – Wind Energy
6. Load Database Construction
In contrast, other patterns came up when analyzing the load behavior changes trough the turbulence
intensity.
Blade Load Estimations by Database for SCADA
S ite In flow C ond ition C harac te riza tionTu
rbul
ence
Inte
nsit
y
2 4 6 8 10 12 14 16 18 20 22 24
24681012141618202224262830
2 4 6 8 10 12 14 16 18 20 22 24
24681012141618202224262830
2940
0
Mean Wind Speed ms
S ite In flow C ond ition C harac te riza tion
Turb
ulen
ceIn
tens
ity
2 4 6 8 10 12 14 16 18 20 22 24
24681012141618202224262830
2 4 6 8 10 12 14 16 18 20 22 24
24681012141618202224262830
2940
0
Mean Wind Speed ms
Mean Wind Speed 7m/s.
Turbulence Intensity:• 9%• 11%• 13%• 15%• 17%
Edgewise
Flapwise
14SET MSc – Wind Energy
6. Load Database Construction
From all the load histograms generated, load distributions functions were constructed; all these were
normalized to 10-min. All the load distribution functions were made by piecewise functions, for the
edgewise case three polynomials were used. For the flapwise functions only two functions were used.
Blade Load Estimations by Database for SCADA
S ite In flow C ond ition C harac te riza tion
Turb
ulen
ceIn
tens
ity
2 4 6 8 10 12 14 16 18 20 22 24
24681012141618202224262830
2 4 6 8 10 12 14 16 18 20 22 24
24681012141618202224262830
2940
0
Mean Wind Speed msTo fit better the tail behavior, a moving average with a ratio of 1:5 was used . The tails were fitted with a linear
or a quadratic function in the logarithmic scale.
15SET MSc – Wind Energy
6. Load Database Construction
Respect to the idling condition, it was characterized only for all the speeds lower the cut-in wind speed. It
was interesting to note the apparent gravity peak pattern seen in the flapwise direction.
Blade Load Estimations by Database for SCADA
The same gravity peak appear at
power production cases with low
winds speeds. It is caused by the
high pitching angles of the
idling conditions.
In the edgewise direction, it
causes the appearance of a
double peak.
16SET MSc – Wind Energy
6. Load Database Construction
From all the load distribution functions load estimators can be derived; they can take form as
equivalent loads, fatigue damages or even maximum load values were obtained. The next are
examples from the fatigue damages normalized for 10-min.
Blade Load Estimations by Database for SCADA
Linear fatigue damage increase with the turbulence intensity for the edgewise direction, exponential for
flapwise.
17SET MSc – Wind Energy
7. Database Estimators Validation
When comparing a single random 10-min. load sequence with the load distributions from the database,
it was observed they does not match well. Scatter appears especially at the tail of the edgewise distribution.
Blade Load Estimations by Database for SCADA
S ite In flow C ond ition C harac te riza tion
Turb
ulen
ceIn
tens
ity
2 4 6 8 10 12 14 16 18 20 22 24
24681012141618202224262830
2 4 6 8 10 12 14 16 18 20 22 24
24681012141618202224262830
2940
0
Mean Wind Speed ms
Furthermore, it was noticed the histogram data points show spaces between bin counts. Not every
5KNm in the cycle load amplitude axis has a count.
18SET MSc – Wind Energy
7. Database Estimators Validation
From the database constructed is possible to estimate the cumulative fatigue of such turbine. It can be
estimated with the database information and compared with the sum of all the 10-min. calculated
fatigue damages.
Blade Load Estimations by Database for SCADA
The error range from 31.4% and 41%. They can be attributed to
the scatter and the missed counts trough each single load
histogram.
From: 650 -700 KNm
7/10 counts
From: 200-300 KNm11/20 counts
19SET MSc – Wind Energy
7. Database Estimators Validation
Blade Load Estimations by Database for SCADA
It was possible to improve the cumulative
fatigue estimation by the use of a
multiplication constant. The main idea was not
to fix the final value of the estimation with the
calculation result, but to make the slope of this
line as similar as possible to the calculation line.
The multiplication constant obtained was
0.835.
With this, the errors diminished to 10.7% and 15%.
Using the database from the turbine 7 data and its
correction, the cumulative fatigue of the turbine 8 was
estimated and its errors range from 9.44 to 10.3%
20SET MSc – Wind Energy
7. Database Estimators Validation
Blade Load Estimations by Database for SCADA
From the database made with the turbine 7 another turbine cumulative fatigue was estimated.
21SET MSc – Wind Energy
7. Database Estimators Validation
Blade Load Estimations by Database for SCADA
For the previous results, all the single fatigue estimation were retrieved from the load database
by means of the reconstructed SCADA data. For this, the pitching angle information is extremely
useful to identify the turbine status. The main statistical values of the wind speed where used as well.
Wind Direction
Power ProductionPitch Angle: 0-25°
Start UpPitch Angle: ~ 45°
Pause, Stop & E. StopPitch Angle: ~ 90°
IdlingPitch Angle: 25-40°
In real life applications, other variables from
the SCADA data, as the electrical power
output or the generator speed, could be
used to corroborate the turbine status.
The load estimators do not necessarily have
to be retrieved from the database each 10-
min. This period can be fixed by the
frequency the SCADA system update its
variables.
22SET MSc – Wind Energy
8. Conclusions
Blade Load Estimations by Database for SCADA
• It was possible to create a load estimation method based on previous turbine measurements and on
SCADA data information.
• The fatigue accumulation estimations from both turbines give back smaller errors than other
methodologies. The errors range from 9 to 15%.
• Estimations by neural networks produce errors ranging from 12 to 22% depending on the number of nodes
used in the network.
• Regression techniques have errors ranging from 2 to 23%.
Nevertheless, the methodology proposed in this report still needs to be validated by more
turbines.
• Given the similar load patterns obtained from different turbines under the same wind conditions,
the method developed could be applied to other couple of turbines.
• Thanks to the cumulative loading estimation of the turbine blades, would help to determine
wheatear or not to extend the turbine service lifetime or modify the turbine maintenance
program, this could mean to be a significant monetary advantage.
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