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

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Scada based condition monitoring

SCADA-Based Condition Monitoring

Joining advanced analytic techniques with turbine engineering

Michael WilkinsonEWEA 2013, Vienna


Scada based condition monitoring1

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


Scada based condition monitoring2

SCADA-Based Condition Monitoring

What signals are available?

Nacelle anemometer wind speed

Yaw angle

Pitch angle

Main bearing temp

Rotor rotational speed

Gearbox bearing temps

Generator bearing temps

Gearbox oil sump temp

Generator rotational speed

Winding temps

Gearbox

Generator

Gate temperatures

Exported power

Hub and pitch

system

Main

bearing

Phase Voltages & Currents

Winding temps

Powerconverter

Transformer

Nacelle internal ambient temp

Cooling system temps

External ambient temp


Comparison of methods temperature trending

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

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

Comparison of Methods: Physical Model

Depends on nacelle and external temperature and cooling system duty

Heat Loss to Surroundings

Heat Loss to Cooling System

WIND TURBINE

DRIVETRAIN

COMPONENT

Model using

export power

Energy

Input

Energy Output

Model using nws3 Include ambient temperature and pressure if available.

T

Frictional Losses Dependent on shaft speed (use rotor speedor generator speed in model)

SCADA System

Model inputs:

Nws3, power, rotor speed,

external temp, cooling system temp

Model output:

Component temp


Comparison of methods conclusions

Comparison of Methods: Conclusions


Comparison of methods conclusions1

Comparison of Methods: Conclusions


Comparison of methods conclusions2

Comparison of Methods: Conclusions


Comparison of methods conclusions3

Comparison of Methods: Conclusions


Comparison of methods conclusions4

Comparison of Methods: Conclusions


Comparison of methods conclusions5

Comparison of Methods: Conclusions


Validation study

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


Validation study example results

Validation Study: Example Results

  • Both charts show different signals on same turbine:

TACTUAL –TMODELLED

TACTUAL –TMODELLED


Validation study results

Validation Study: Results


Validation study results1

Validation Study: Results


Validation study results2

Validation Study: Results

  • Two thirds of failures detected in advance


Validation study results3

Validation Study: Results

  • Majority of failures detected 4 to 12 months in advance


Summary conclusions

Summary & Conclusions

  • Comparison of methods:

    • Temperature trending, physical model and artificial neural network methods compared

    • Physical model identified as most promising


Summary conclusions1

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 conclusions2

    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


  • Scada based condition monitoring

    Questions or comments?Michael WilkinsonGL Garrad Hassan+44 117 972 [email protected]


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