Abstract:
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The reliability requirements of wind turbine (WT) components have increased
significantly in recent years in the search for a lower impact on the cost of energy. In
addition, the trend towards larger WTs installed in offshore locations has significantly
increased the cost of repair of the components. In the wind industry, therefore, condition
monitoring is crucial for maximum availability.
In this work, without using specific tailored devices for condition monitoring but only
increasing the sampling frequency in the already available sensors of the SCADA system,
a data-driven multi-fault diagnosis strategy is contributed. The advanced WT benchmark
proposed by [1] is used. That is a 5 MW modern WT simulated with the FAST [2]
software and subject to various actuators and sensors faults of different type. The
measurement noise at each sensor is modeled as a Gaussian white noise.
First, the SCADA measurements are pre-processed and feature transformation based on
multiway principal component analysis (MPCA) is realized. Then, 10-fold cross
validation support vector machines (SVM) based classification is applied. In this work,
SVMs were used as a first choice for fault detection as they have proven their robustness
for some particular faults [3-5] but never accomplished, to the authors’ knowledge, at the
same time the detection and classification of all the proposed faults taken into account in
this work. To this end, the choice of the features as well as the selection of data are of
primary importance.
Simulation results show that all studied faults are detected and classified with an overall
accuracy of 98%. Finally, it is noteworthy that the prediction speed allows this strategy
to be deployed for real-time condition monitoring in WTs. |