PREDICTIVE ENGINEERING IN WIND ENERGY: A DATA-MINING APPROACH

PREDICTIVE ENGINEERING IN WIND ENERGY: A DATA-MINING APPROACH

Student: Wenyan (Emily) Li

PI: Andrew Kusiak

Feb.12, 2010

OutlinePredictive Engineering in Wind Energy: Data-driven Approach Overview

Case StudyShort Term Prediction of Wind Turbine Parameters

Dynamic Control of Wind Turbines

Current ChallengePrediction and Diagnosis of Wind Turbine Faults

Predictive Engineering in Wind Energy: Data-driven Approach Overview

Main TopicsData Description

Main TopicsExisting approaches primarily use statistical methods.Data mining approach:

Power Prediction and OptimizationWind Speed ForecastingControl of Wind TurbinesVibration Analysis of Wind Turbine ComponentsCondition Monitoring and Fault Diagnosis

Data DescriptionThe data used in the research was generated at a wind farm with about 100 turbines. The data was collected by a SCADA (Supervisory Control and Data Acquisition) system installed at each wind turbine. Each SCADA system collects data for more than 120 parameters.

Controllable parameters, e.g., blade pitch angle, generator torque

Non-controllable parameters, e.g., wind speed

Turbine performance parameters, e.g., power output, rotor speed.

Though the data is sampled at a high frequency, it is normally averaged and stored at a lower frequency, such as 10-minute intervals or 10-second intervals.

Data Description

Data DescriptionTime Power

(Actual)Wind Speed

Generator Speed

Rotor Speed

Blade 1, actual value

Torque, actual value …

4:35:20 371.6 6.74 1409.6 19.55 0.07 25.7 …

4:35:30 417.9 6.77 1430.2 19.88 0.07 27.97 …

4:35:40 389.4 6.47 1426.5 19.82 0.07 26.4 …

4:35:50 369.6 6.32 1412.3 19.64 0.07 25.51 …

4:36:00 372.6 6.24 1409.2 19.57 0.07 25.52 …

4:36:10 422.4 6.93 1430 19.89 0.07 28.24 …

4:36:20 403.3 6.7 1429 19.88 0.07 27.17 …

4:36:30 381.8 6.38 1419.7 19.73 0.07 25.75 …

4:36:40 361.2 6.11 1398 19.45 0.07 24.83 …

4:36:50 339.2 6.04 1367.7 19.05 0.07 24.02 …

… … … … … … … …

BasicsPhysics-based equations:

Parameter Type

Parameter Name Abbreviation Symbol Unit

Non-controllable Wind speed WS m/s

Controllable

Blade pitch angle BPA °

Generator torque GT Nm

PerformancePower output PO kW

Rotor speed RS rpm

( )v t

1( )x t

2 ( )x t

1( )y t

2 ( )y t

a rP T rRv

2 31 ( , )2a pP R C v

Table 6. Parameter selection for clustering in wind power prediction

OutlinePredictive Engineering in Wind Energy: Data-driven Approach Overview




Short Term Prediction of Wind Turbine Parameters

Background

Methodology

Feature Selection

Data Sampling

Model Construction

Computational Results

Background

Considering the fact that wind speeds and wind turbine performance vary across different turbine locations of a wind farm, the question arises as to whether a generalized model (called here a virtual model) of a wind turbine could be developed.

Such a virtual model has been developed based on SCADA data collected at wind turbines.

Methodology

Methodology for developing virtual models

Data Collection: An Ideal Power Curve

Ideal power curve

Data Collection: Actual Power Curve

0

400

800

1200

1600

0 5 10 15 20

Pow

er O

utpu

t [kW

]

Wind Speed [m/s]

1

2

3

4

5

6

1 10 19 28 37 46 55

Win

d sp

eed

(m/s

)

Turbine 1 Turbine 2 Turbine 3 Turbine 4

-20

20

60

100

140

180

3.5 4 4.5 5 5.5 6

Pow

er o

utpu

t (kW

)

Wind speed (m/s) Turbine 1 Turbine 2 Turbine 3 Turbine 4

-20

80

180

280

380

2 3 4 5 6 7 8

Pow

er o

utpu

t (kW

)

Wind speed (m/s) Turbine 1 Turbine 2 Turbine 3 Turbine 4

Actual wind speed and power curve

Parameter Selection

Selection method: Combined domain knowledge and data mining algorithms

The process of parameter selection

Data Sampling96.75

82.68

0

20

40

60

80

100

3.5 4.5 5.5 6.5 7.5 8.5 9.5 10.5 11.5 12.5 14.0 16.0 18.0 20.0 21.0

Cum

ulat

ive p

erce

ntag

e (%

)

Wind speed (m/s) Low wind speed High wind speed

88.50

56.03

0

20

40

60

80

100

100 300 500 700 900 1100 1300 1500

Cum

ulat

ive p

erce

ntag

e (%

)

Power output (kW) Low wind speed High wind speed

0.087.82 8.65

41.14

87.66

31.19

86.38

100.00

0

20

40

60

80

100

0 0-5 5-10 10-15 15-20 20-23

Cum

ulat

ive p

erce

ntag

e (%

)

Rotor speed (rpm) Low wind speed High wind speed

As the wind speed in the interval [3.5-13] m/s is studied, 1500 data points were randomly selected from low wind speed data set in each category of the wind speed data to form a training data.

Figure 11. Illustration of data sampling.

Model Building1 1 1 1 2 2 1 1

1 2 2 2

( ) ( ( 1), ( 2), ( 1), ( 2), ( ), ( 1),( 2) ( ), ( 1), ( 2) ( ), ( 1) ( 2) ( 6))y t f y t y t y t y t x t x tx t x t x t x t v t v t v t v t

2 2 1 1 2 2 1 1

1 2 2 2

( ) ( ( 1), ( 2), ( 1), ( 2) ( ), ( 1),( 2) ( ), ( 1), ( 2), ( ), ( 1) ( 2) ( 6))y t f y t y t y t y t x t x tx t x t x t x t v t v t v t v t

Model extraction using different algorithms


Prediction results of power output Prediction results of rotor speed

Outline






Background

Dynamic Model

Adjusting Objectives based on Wind Conditions and Operational Requirements


Illustration of Operational Scenarios

Background

Most optimization problems aim to maximize power output of wind turbine;In reality, there are a number of optimal objectives;The model considered in this paper considers five weighted objectives. The weights are adjusted according to eight typical scenarios defined by wind conditions and operational requirements.

Dynamic Model

1 2 3 _ 4 _ 5 _Power Rotor P ramp G ramp Pitch rampJ w J w J w J w J w J

PowerJ

RotorJ

_P rampJ

_G rampJ

_Pitch rampJ

is a function to minimize the distance between the power output to its upper limit and therefore maximizing the power output;is a function to minimize rotor speed ramp;

is a function to minimize power output ramp;

is a function to minimize generation torque ramp;

is a function to minimize pitch angle ramp;

1 2 3 4 5 _ _ _min ( , , , , , , , , , )Power Rotor P ramp G ramp Pitch rampJ w w w w w J J J J J

1 2 3 4 5 1w w w w w


Turbulence Intensity is used as the threshold to distinguish between high turbulence intensity and low turbulence intensity

Wind Speed

The speed of is used as an threshold to distinguish between high wind speed and low wind speed

Electricity demand

High and low electricity demand (arbitrarily)

0.06tI

7 /m s


Adjust weights according to the scenario category

Weights (arbitrarily) distribution for eight scenarios


Scenario

Number

Original Average

Power Output [kW]

Optimized Average Power

Output [kW]

Original STD of Power Output

[kW]

Optimized STD of Power Output

[kW]

1795.01

1278.36171.99

153.80

2 1098.36 87.24

3907.60

1427.70228.90

125.61

4 554.90 55.69

5200.40

372.62139.82

226.93

6 271.72 153.23

7275.00

535.5536.97

64.58

8 498.89 62.51

Summary of power output generation


Scenario

Number

Original STD of Rotor

Speed [rpm]

Optimized STD of

Rotor Speed [rpm]

Original STD of Blade

Pitch Angle [°]

Optimized STD of

Blade Pitch Angle [°]

Original STD of

Generator Torque [Nm]

Optimized STD of

Generator Torque [Nm]

10.25

0.244.39

1.911152.89

881.82

2 0.10 2.47 563.29

30.37

0.273.57

3.961470.40

747.91

4 0.09 2.15 400.58

51.79

1.694.09

4.54934.38

1614.48

6 1.39 4.34 1142.18

70.82

0.300.00

2.36207.19

438.80

8 0.40 0.72 446.31

Summary of power output generation

Operational Scenario 4: High Wind Speed, Low Turbulence Intensity, and Low Electricity Demand

400

600

800

1000

1200

1400

1:05:40 PM 1:06:40 PM 1:07:40 PM 1:08:40 PM 1:09:40 PM

Pow

er O

utpu

t (kW

)

Optimized Original

17.5

18.0

18.5

19.0

19.5

20.0

1:05:40 PM 1:06:40 PM 1:07:40 PM 1:08:40 PM 1:09:40 PM

Rot

or sp

eed

(RPM

)

Optimized Original

-2

0

2

4

6

8

10

12

1:05:40 PM 1:06:40 PM 1:07:40 PM 1:08:40 PM 1:09:40 PM

Pitc

h an

gle

(deg

ree)

Optimized Original

3000

4000

5000

6000

7000

8000

9000

1:05:40 PM 1:06:40 PM 1:07:40 PM 1:08:40 PM 1:09:40 PM

Gen

erat

or to

rque

(Nm

)

Optimized Original

Optimal results for operational scenario 4

Operational Scenario 7: Low Wind Speed, Low Turbulence Intensity, and High Electricity Demand

200

250

300

350

400

450

500

550

600

650

4:36:20 AM 4:37:10 AM 4:38:00 AM 4:38:50 AM 4:39:40 AM

Pow

er O

utpu

t (kW

)

Optimized Original

16

17

18

19

20

21

4:36:20 AM 4:37:10 AM 4:38:00 AM 4:38:50 AM 4:39:40 AM

Rot

or sp

eed

(RPM

)

Optimized Original

0

2

4

6

8

10

4:36:20 AM 4:37:10 AM 4:38:00 AM 4:38:50 AM 4:39:40 AM

Pitc

h an

gle

(deg

ree)

Optimized Original

1000

2000

3000

4000

5000

4:36:20 AM 4:37:10 AM 4:38:00 AM 4:38:50 AM 4:39:40 AM

Gen

erat

or to

rque

(Nm

)

Optimized Original

Optimal results for operational scenario 7

Outline





Prediction and Diagnosis of Wind Turbine Faults

Background

Data Description

Current Methodology

Current Challenge

Solution?

Introduction

The expansion of wind power has increased interest in operations and maintenance of wind turbines.

The operations, maintenance, and part replacement costs are expensive when wind turbine or its components break down.

Condition monitoring and fault diagnosis of wind turbines are of high priority.

Data DescriptionTwo separate data files:

Original SCADA data at 5-min intervals;

Status/Fault data is provided when the fault occurs;

Parameter Name Definition

Fault time Date and time of the fault occurrence

Status code Status code assigned to the fault occurred

Category Category of the status code (four categories)

Generator speed Generator speed at the time the fault has occurred

Power output Power production at the time the fault has occurred

Wind speed Wind speed at the time the fault has occurred

Parameters related to the fault information

Data DescriptionFault distribution of a random turbine

11 20 4 3142131

60

1096

0

200

400

600

800

1000

1200

1 2 3 4

Freq

uenc

y

Category

Fault category Fault

There are 35 specific faults (11 in category 1 + 20 in category 2 + 4 in category 3) and 31 different status occurrences. In total 233 (42 + 131 + 60) faults and 1096 statuses are captured during the three month period.

Fault distribution for a random turbine

Current MethodThree-level fault prediction

General process for each level:

Level 1:Predict

status/fault

Level 2:Predict

category of status/fault

Level 3: Predict

specific fault

Levels for fault prediction

Process of fault prediction

Step 1:Labeling

SCADA data

Step 3:Model

extraction

Step 4:Analysis of

computational results

Step 2:Data sampling

Current Challenge

Limited fault data

Data sampling is not representative

Difficult to distinguish fault category

Difficult to predict specific fault

Solution?

PREDICTIVE ENGINEERING IN WIND ENERGY: A DATA-MINING APPROACH

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