scada-based condition monitoring
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SCADA-Based Condition Monitoring. Joining advanced analytic techniques with turbine engineering Michael Wilkinson EWEA 2013, Vienna. SCADA-Based Condition Monitoring. What is it? Failure detection algorithm that uses existing SCADA data - PowerPoint PPT PresentationTRANSCRIPT
SCADA-Based Condition Monitoring Joining advanced analytic techniques with turbine engineering Michael Wilkinson EWEA 2013, Vienna
SCADA-Based Condition Monitoring
What is it?• Failure detection algorithm that uses
existing SCADA data• Uses an established relationship
between SCADA signals to detect when a component is operating abnormally
• Compares suspected failures to a database of known issues to determine likelihood of an emerging problem
What is it not?• High-frequency vibration monitoring• An automatic algorithm
Winding temps
Winding temps
Main bearing temp
Gearbox bearing temps
Gearbox oil sump tempGenerator bearing tempsGenerator rotational speed
Gate temperatures
Phase Voltages & Currents
Nacelle internal ambient tempCooling system tempsExternal ambient temp
SCADA-Based Condition Monitoring
Mainbearing
Pitch angle
Rotor rotational speed
Exported power
Nacelle anemometer wind speedYaw angle
Hub and pitchsystem
Gearbox Generator
Power converter Transformer
What signals are available?
Comparison of Methods: Temperature Trending
• Simple method• Readily applied to many datasets• Low reliability during intermittent or changing operational modes
Comparison of Methods: Artificial Neural Networks
• Learning algorithm used to reveal patterns in data or model complex relationships between variables
• More sensitive to ‘abnormal’ behaviour
• Inability to identify nature of the operational issue
• Results difficult to interpret
Comparison of Methods: Physical Model
Heat Loss to Surroundings
Heat Loss to Cooling System
Depends on nacelle and external temperature and cooling system duty
Model using nws3 Include ambient temperature and pressure if
available.Frictional Losses Dependent on shaft
speed (use rotor speedor generator speed in
model)
T
SCADA System
Energy Output
WIND TURBINEDRIVETRAINCOMPONENT
EnergyInput
Model inputs:Nws3, power, rotor speed,external temp, cooling system temp
Model output:Component temp
Model usingexport power
Comparison of Methods: Conclusions
Criteria Signal Trending SOM Physical
Model
Time and effort to initiate a new model for each turbine analysis 3 2 1
Comparison of Methods: Conclusions
Criteria Signal Trending SOM Physical
Model
Time and effort to initiate a new model for each turbine analysis 3 2 1
Ability to incorporate a wide range of model inputs 1 3 2
Comparison of Methods: Conclusions
Criteria Signal Trending SOM Physical
Model
Time and effort to initiate a new model for each turbine analysis 3 2 1
Ability to incorporate a wide range of model inputs 1 3 2
Ease of identifying impending component failure 2 1 3
Comparison of Methods: Conclusions
Criteria Signal Trending SOM Physical
Model
Time and effort to initiate a new model for each turbine analysis 3 2 1
Ability to incorporate a wide range of model inputs 1 3 2
Ease of identifying impending component failure 2 1 3
Ability to distinguish component deterioration from operational or environmental fluctuations
1 2 3
Comparison of Methods: Conclusions
Criteria Signal Trending SOM Physical
Model
Time and effort to initiate a new model for each turbine analysis 3 2 1
Ability to incorporate a wide range of model inputs 1 3 2
Ease of identifying impending component failure 2 1 3
Ability to distinguish component deterioration from operational or environmental fluctuations
1 2 3
Ability to detect impending failures in advance 2 1 3
Comparison of Methods: Conclusions
Criteria Signal Trending SOM Physical
Model
Time and effort to initiate a new model for each turbine analysis 3 2 1
Ability to incorporate a wide range of model inputs 1 3 2
Ease of identifying impending component failure 2 1 3
Ability to distinguish component deterioration from operational or environmental fluctuations
1 2 3
Ability to detect impending failures in advance 2 1 3
Total Score 9 9 12
Validation Study
Series of blind tests were conducted• Historical data• Engineer given no indication of known failures• Suspected impending failures documented
• 472 turbine-years of data considered
• Compared against service records and site management reports
Site Location Operational Data Set
YearsA Italy 4.8B Ireland 6.0C Ireland 6.5D UK 7.0E UK 2.5
Validation Study: Example Results
• Both charts show different signals on same turbine:
Modelled Temperature
Rotor Side High Speed Bearing
Model Inputs Generator Speed Power Nacelle Temperature
Failed Component GearboxAdvance notice 9 months
Modelled Temperature
Gen Side Intermediate Speed Bearing
Model Inputs Generator Speed Power Nacelle Temperature
Failed Component GearboxAdvance notice 7 months
T AC
TUA
L –T M
OD
ELL
ED
T AC
TUA
L –T M
OD
ELL
ED
Validation Study: Results
Site Location Operational Data Set
Years
Predicted failures
A Italy 4.8 7B Ireland 6.0 7C Ireland 6.5 1D UK 7.0 5E UK 2.5 7
Validation Study: Results
Site Location Operational Data Set
Years
Predicted failures
Actual Failures
True Detections
False Detections
Score True / False
A Italy 4.8 7 8 7 0 88% / 0%B Ireland 6.0 7 8 6 1 75% / 13%C Ireland 6.5 1 4 1 0 25% / 0%D UK 7.0 5 6 5 0 83% / 0%E UK 2.5 7 10 5 2 50% / 20%
Validation Study: Results
Site Location Operational Data Set
Years
Predicted failures
Actual Failures
True Detections
False Detections
Score True / False
A Italy 4.8 7 8 7 0 88% / 0%B Ireland 6.0 7 8 6 1 75% / 13%C Ireland 6.5 1 4 1 0 25% / 0%D UK 7.0 5 6 5 0 83% / 0%E UK 2.5 7 10 5 2 50% / 20%
Two thirds of failures detected
in advance
Validation Study: Results
Majority of failures detected 4 to 12 months in advance
Summary & Conclusions
• Comparison of methods:• Temperature trending, physical model and artificial neural network methods compared• Physical model identified as most promising
Summary & Conclusions
• Comparison of methods:• Temperature trending, physical model and artificial neural network methods compared• Physical model identified as most promising
• Validation study performed:• Two thirds of failures detected in advance• Majority of failures detected 4 to 12 months in advance
Summary & Conclusions
• Comparison of methods:• Temperature trending, physical model and artificial neural network methods compared• Physical model identified as most promising
• Validation study performed:• Two thirds of failures detected in advance• Majority of failures detected 4 to 12 months in advance
• Overall conclusions:• Quick implementation – no additional monitoring hardware required• Pro-active maintenance/repair activities to be scheduled and planned• Targeted inspections possible
