Blade Load Estimations by a Load Database for an Implementation in SCADA Systems

21-04-23

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


Z

Y

X

1. Introduction

FT(V,u,z)

FC

FG

ΩQ(V)

MY(Ω)

• Real Wind Conditions


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


• 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


• 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.


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.


How accurate are the fatigue damages and the cumulative fatigue estimations when comparing them against other load estimation methods results?

Neural NetworksRegression Techniques


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.


Key Signals Measured (32Hz):

•Stain signals from the root of the

blade

• Edgewise

• Flapwise

•Other 70 signals

• Standard signals

Standard Reconstruction of SCADA data


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.


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


4. Method



4. Method


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.


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.


• Turbine 8

• Turbine 7



0 200 400 600 800

0.01

0.1

1

10

100

Cycle Amplitude KNm

Ocurrences

NFlap


0 200 400 600 800

0.01

0.1

1

10

100

Cycle Amplitude KNm

Ocurrences

NFlap



0 200 400 600 800

0.01

0.1

1

10

100

Cycle Amplitude KNm

Ocurrences

NFlap


0 200 400 600 800

0.01

0.1

1

10

100

Cycle Amplitude KNm

Ocurrences

NFlap

5. Load Comparison Between Turbines




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.



In contrast, other patterns came up when analyzing the load behavior changes trough the turbulence

intensity.


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


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



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.



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.



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.


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.



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.


Linear fatigue damage increase with the turbulence intensity for the edgewise direction, exponential for

flapwise.



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.



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.



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.


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




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%




From the database made with the turbine 7 another turbine cumulative fatigue was estimated.




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.


8. Conclusions


• 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.

23SET MSc – Wind EnergyEsbjerg, Support Structure DesignEsbjerg, Support Structure DesignThe New York–Long Island 340MW Project

Thanks for the Attention

Questions…?


Blade Load Estimations by a Load Database for an Implementation in SCADA Systems

Documents

Transcript of Blade Load Estimations by a Load Database for an Implementation in SCADA Systems