Machine learning techniques for wind turbine fault diagnosis
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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  is used. That is a 5 MW modern WT simulated with the FAST  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.
CitacióVidal, Y., Pozo, F., Luo, N., Tutivén, C., Rodellar, J. Machine learning techniques for wind turbine fault diagnosis. A: World Conference on Structural Control and Monitoring. "Control and monitoring: abstracts and papers of the 7th World Conference on Structural Control and Monitoring: 7WCSCM: July 22-25, 2018 Qingdao, China". 2018, p. 1385-1394.
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