Data-driven fault identification of ageing wind turbine

Yue Liu, Long Zhang

Research output: Contribution to conferencePaperpeer-review


This paper proposes an ageing evaluation method
for wind turbine system by using a data driven method. This
method directly uses the input and output data of the wind
turbine system, and the autoregressive with exogenous (ARX)
model to identify the wind turbine system. The input and
output data include wind speed, generated power, and pitch
angle, and they are generated by a wind turbine simulation
model with four ageing cases: mechanical power, magnetizing
inductance, pitch angle controller gain and pitch angle change
rate. By using the generated power and pitch angle data of
wind turbine under different ageing levels, the data-driven
models can be obtained. By comparing the model parameters
in different states identified by the ARX model, results show
that the degree of ageing can be reflected by the parameter
changes. This demonstrates that the method can detect the
ageing of wind turbines
Original languageEnglish
Publication statusAccepted/In press - Mar 2022


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