scada data interpretation improves wind farm maintenance · scada data interpretation improves wind...

2015/2/10 EERA DeepWind’2015 1

SCADA Data Interpretation improves Wind Farm Maintenance

Professor Kesheng Wang [email protected]

Knowledhe Discovery Laboratory Department of Production and Quality Engineering Norwegian University of Science and Technology

mailto:[email protected]

Outlines

Introduction Predictive Maintenance Framework of WINDSENSE Project SCADA Data Based CMS Case study Conclusions

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Introduction Renewable energy sources are playing an important role in

the global energy mix, as a means of reducing the impact of energy production on climate change. Wind energy is most developed renewable energy techniques.

The management of wind farms is challenging because it involves several difficult tasks, such as wind forecasting and the operations and maintenance of turbines.

The maintenance of wind turbines has received attention in recent years due to its impact on the cost of generating power from wind. The main tendency of maintenance policy is changing from Preventive Maintenance (PM) and Corrective Maintenance (CM), to Predictive Maintenance (PdM).


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Classification of Maintenance Policy


Fault Diagnosis and Prognosis Systems on Wind Turbines

Major Failures on Wind Turbines: http://www.ifm.com/obj/ifm_wind_power_CMS_EN.pdf


Mechanical component

Failure percentage

Main Gearbox 32 %

Generator 23 %

Main Bearing 11 %

Rotor Blades* <10%

*Blade resonances lead to fatigue failures

http://www.ifm.com/obj/ifm_wind_power_CMS_EN.pdf

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Framework for WINDSENSE - (Add-on instrumentation system for wind trubines)


Wind Turbines

Degradation Process

Feature Extraction

Fault Diagnosis

Fault PrognosisMaintenance Scheduling / Maintenance Optimization

Signal Pre-process

Denosing Time Domain

Time-Frequency Domain

Frequency Domain (FFT, DFT)

Wavelet Domain (WT, WPT)

Principal Component Analysis (PCA)

Compression

Extract Weak Signal

Filter

Amplification

Support Support Machine (SVM)

Data Mining (Decision Tree & Association rules)

Artificial Neural Network (SOM & SBP)

Statistical Maching

Auto-regressive Moving Averaging (ARMA)

Fuzzy Logic Prediction

ANN Prediction

Match Matrix Prediction

Ant Colony Optimization (ACO)

Particle Swarm Optimization (PSO)

Gentic Algorithms (GA)

Meta-Heuristic approaches

Bee Colony Algorithms (BCA)

Key Performance Indicator (KPI)

KPI Leading

KPI Logging

Maintenance Management

System

Onshore Wind Turbine

Offshore Wind Turbine

Collapsed Wind Turbine

Data Acquisition

Fiber Bragg Grating Sensors

Acoustic Emission(AE)Sensors

Ultrasonic Sensors + AE

Vibration sensors

Wireless Data Collection Networks

SCADA Data Based CMS for WT SCADA System:


First Generation SCADA Architecture Second Generation SCADA Architecture

Third Generation SCADA System Typical SCADA System

SCADA Data Based CMS for WT


Sensor 1 Micoprocessor

mounted on device

Sensor 2 Micoprocessor

Sensor N Micoprocessor

Centralised SCADA System

SCADA DATARAW DATA

SCADA master

10 mins average data

Fault diagnosis

Fault prognosis

Maintenance

DATA MINING

HMI

Corroborate

PAST/PRESENT DATA

PRESENT DATA

Operator Workstation

WANRTU remote location

and/orPLC

Strategy

Algorithmsand/ormodels

Raw Data and SCADA Data (proposed frame work)

SCADA Dataset Description Wind parameters, such as wind speed and wind direction; Performance parameters, such as power output, rotor speed, and blade

pitch angle; Vibration parameters, such as tower acceleration and drive train

acceleration; and Temperature parameters, such as bearing temperature and gearbox

temperature. Examples

Active power output (10 min max/min/average) Anemometer-measured wind speed (10 min max/min/average) Turbine speed (10 min max/min/average) Nacelle temperature (10 min max/min/average) Turbine rear bearing temperature (10 min max/min/average) Turbine rear bearing vibration (10 min RMS max/min/average) Turbine front bearing temperature (10 min max/min/average) Turbine front vibration (10 min RMS max/min/average) …

10.02.2015 WORLD CLASS - through people, technology and dedication

ANN-based Modeling of SCADA Parameter Normal Behavior

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Procedure of Fault Detection based on SCADA Data

Case Study

Indicator: Temperature Possible parameters influence or relate to

indicator: Rear bearing temperature (t-1) Active power output (t) Nacelle temperature (t) Turbine speed (t) Cooling fan status


Rear Bearing

Model Output Input

Rear Bearing Temperature

Rear bearing temperature (t-1) Active power output (t) Nacelle temperature (t) Turbine speed (t)

Input and Outputs of ANN Model

Parameter Selection

ANN Model Training

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2009-05-01 2009-06-01 2009-07-0120

25

30

35

40

Tem

pera

ture

(Cels

ius D

egre

e)

(a)

Turbine Rear Bearing Temperature

2009-05-01 2009-06-01 2009-07-010

5

10

15

Turbine Speed

(b)

Turb

ine S

peed

(RPM

)

2009-05-01 2009-06-01 2009-07-01

18

20

22

24

26

28

30Nacelle Temperature

(c)

Tem

pera

ture

(Cels

ius D

egre

e)

2009-05-01 2009-06-01 2009-07-010

1000

2000

3000

(d)

Activ

e Po

wer (

KW))

Active Power

Time (yyyy-mm-dd)

Rear Bearing Temperature Model Training Data

ANN Model Testing


2009-05-31 2009-06-07 2009-06-14 2009-06-21 2009-06-28 2009-07-05 2009-07-12 2009-07-19 2009-07-2625

30

35

40

45

(a)

Real Temperature and Estimated Temperature

EstimatedTempBearTemp

09-05-31 09-06-07 09-06-14 09-06-21 09-06-28 09-07-05 09-07-12 09-07-19 09-07-26-3

-2

-1

0

1

2

3Difference Between Actual and Estimated Temperature

(b)

Time (yyyy-mm-dd)

Rear Bearing Model Output in Normal Condition

2009-05-31 2009-06-07 2009-06-14 2009-06-21 2009-06-28 2009-07-05 2009-07-12 2009-07-19 2009-07-2625

30

35

40

45

(a)

Tem

pera

ture

(Cel

sius

Deg

ree)

Turbine Rear Bearing Temperature (t-1)

2009-05-31 2009-06-07 2009-06-14 2009-06-21 2009-06-28 2009-07-05 2009-07-12 2009-07-19 2009-07-260

5

10

15

20

(b)

Turb

ine

Spee

d (R

PM)

Turbine Speed

2009-05-31 2009-06-07 2009-06-14 2009-06-21 2009-06-28 2009-07-05 2009-07-12 2009-07-19 2009-07-2615

20

25

30

35

(c)

Tem

pera

ture

(Cel

sius

Deg

ree)

Nacelle Temperature

2009-05-31 2009-06-07 2009-06-14 2009-06-21 2009-06-28 2009-07-05 2009-07-12 2009-07-19 2009-07-260

1000

2000

3000

4000

(d)

Activ

e Po

wer (

KW))

Active Power

Time (yyyy-mm-dd)

Rear Bearing Model Testing Input Data

Test Data: 26.05.2009 to 26.07.2009

Detection of Rear Bearing Fault

①: The first important deviation from the model estimates occurred from the start of October 2010.

③: the turbine was stopped because of overheating.

The operator try to solve the problem two times in point ③ and ④ but not successful.

⑤: the turbine was completely stopped because of the overheating

3 months early warning 10 days close alarm


2010-08-01 2010-09-01 2010-10-01 2010-11-01 2010-12-01 2011-01-01 2011-02-01 2011-03-01

10

20

30

40

50

60

(a)

Actual and Estimated Temperature


2010-08-01 2010-09-01 2010-10-01 2010-11-01 2010-12-01 2011-01-01 2011-02-01 2011-03-01-5

0

5

10

15Difference Between Actual and Estimated Temperature

(b)

Time (yyyy-mm-dd)

4

52 3

1

4

1.5

Fault Detection Results of Rear Bearing

Discussion Whether the model established from the SCADA data of one turbine, can be applied in fault detection to another turbines?


2010-07-01 2010-08-01 2010-09-01 2010-10-01

25

30

35

40

(a)

Turbine Bearing Temperature (t-1)

2010-07-01 2010-08-01 2010-09-01 2010-10-010

5

10

15

(b)

Turbine Speed

2010-07-01 2010-08-01 2010-09-01 2010-10-010

10

20

30

(c)

Nacelle Temperature

2010-07-01 2010-08-01 2010-09-01 2010-10-010

1000

2000

3000

Active Power

(d)

Time (yyyy-mm-dd)

Rear Bearing Model Testing Input Data of New Turbine

2010-07-01 2010-08-01 2010-09-01 2010-10-0115

20

25

30

35

40

45

50Actual and Estimated Temerature


2010-07-01 2010-08-01 2010-09-01 2010-10-01-2

-1.5

-1

-0.5

0

0.5

1

1.5

2Difference between Actual and Estimated Temerature

Yes, with the same type of turbines

Conclusions (1) The accuracy of the diagnosis model depends upon the input data. Thus a

careful selection of variables and the quality of the data (free from noise) are the prime factors affecting accuracy. Hence adequate pre-processing models are desirable. Though the comparison results of various models is mentioned but no one clear cut perfect modeling technique could emerge. So this area needs further explorations. Hence a lot is to be done in this area, in order to obtain a generic model for CMS using SCADA data.

The most of the research has been carried out using low frequency SCADA data (10 minutes average) and for a better prediction higher quality data is required (high frequency, noise free and long duration). This is another point which needs to be explored, i.e. what frequency SCADA data gives optimal results in case of WTs.

It is proposed to diagnose and prognosticate with both conventional data and SCADA data and with the comparison of the two results. The suitable maintenance strategy could then be worked out.

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Conclusions (2) Furthermore the relationships between faults have not been explored. It has been demonstrated that with combination of appropriate data mining

algorithms and computational intelligence concepts the accuracy and robustness of model is enhanced.

Most of the researchers have pinpointed that the data sharing by the wind engineering industry was major hindrance in this type of research. Hence there is a strong need for a pooling of common data base for researchers across the globe.

Based upon the CMS of WT’s using SCADA data appropriate maintenance strategy could be worked in order to achieve the goal of PdM.

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scada data interpretation improves wind farm maintenance · scada data interpretation improves wind...

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