This document describes a project to optimize early failure prediction for wind turbines using data analytics. A team analyzed data from wind turbine control systems to develop an artificial intelligence model that can detect potential failures by noticing deviations between actual and predicted parameter measurements. The goals are to reduce repair and replacement costs from unexpected failures and make relevant data accessible to operators and professionals to improve efficiency. The team mapped causal relationships between operational parameters and trained a model to predict values based on those relationships to flag unexpected divergences that may indicate upcoming issues.

1. wind turbine
relationship
management
system
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Team members:
Carlos Poblacion
Daniel Van der Maas
Giovanni Battista Picotti
Nicolas Juguet
Rosalie Van der Maas
Helping people optimise early prediction and data enhancement

2. Problem
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Wind Turbine Operators
lose key amounts of money
on repairs, replacements
and production losses due
to failures in the wind
turbines

3. Current practice
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Wind turbines generate huge amount of data from the SCADA
control system. These data need to be analysed and interpreted

4. Artificial Intelligence
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Analysing the data stream and notifying professionals when
something requires a human eye

5. Data Analytics
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6. Data driven working
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Two key things to successful data driven working
• Getting all relevant data in order
• Making relevant data available to the right people

7. Optimise early prediction through data enhancement and accessibility
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… don’t be satisfied with just an AI. Having the code WON’T generate savings.
An AI plus a system allowing you to work efficiently in a data driven way WILL!

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Business Model Canvas

11. How we did it - 1
First we mapped out what
parameters have causal effects (not
just correlated) on other parameters.
For example, we expect the ambient
temperature and rotor RPM to
causally effect the gearbox oil
temperature.
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12. How we did it - 2
GBX Oil
Temperature
Rotor RPM
Generator
RPM
Ambient
Temperatur
e
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Next we determined which
causal effects might be altered
due to an upcoming failure.
In our case we might expect
that an upcoming failure may
somehow effect the relation
between the gearbox oil
temperature and rotor RPM

13. How we did it - 3
Finally, we trained an AI
model that predicts the
parameter based on the
parameters that have a
causal effect on it. The model
is trained on data where
there is no upcoming failure.
Whenever the actual
measured value starts to
divert from the predicted
value, there is reason to
expect an upcoming failure.
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