Machine learning algorithms for early fault detection of wind turbines using 10-minute SCADA data are attracting attention in the wind energy community due to their cost-effectiveness. It has been recently shown that convolutional neural networks (CNNs) can significantly improve the performance of such algorithms. One practical aspect in the deployment of these algorithms is that they require a large amount of historical SCADA data for training. These are not always available, for example in the case of newly installed turbines. Here we suggest a cross-turbine training scheme for CNNs: we train a CNN model on a turbine with abundant data and use the trained network to detect faults in a different wind turbine for which only little data are available. We show that this scheme is able to detect faults with an accuracy and robustness which are very similar to the single-turbine scheme, in which training and detection are both done on the same turbine. We demonstrate this for two different fault types: abrupt and slowly evolving faults and perform a sensitivity analysis in order to compare the performance of the two training schemes. We show that the scheme works successfully also when training on turbines from another farm and with different measured variables than the target turbine.
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Fault Detection, Convolutional Neural Networks, Transfer Learning, Wind Turbines, SCADA data.
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