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Wind power, as an alternative to burning fossil fuels, is abundant andinexhaustible. To fully utilize wind power, wind farms are usually located inareas of high altitude and facing serious ice conditions, which can lead toserious consequences. Quick detection of blade ice accretion is crucial for themaintenance of wind farms. Unlike traditional methods of installing expensivephysical detectors on wind blades, data-driven approaches are increasinglypopular for inspecting the wind turbine failures. In this work, we propose awavelet enhanced autoencoder model (WaveletAE) to identify wind turbinedysfunction by analyzing the multivariate time series monitored by the SCADAsystem. WaveletAE is enhanced with wavelet detail coefficients to enforce theautoencoder to capture information from multiple scales, and the CNN-LSTMarchitecture is applied to learn channel-wise and temporal-wise relations. Theempirical study shows that the proposed model outperforms otherstate-of-the-art time series anomaly detection methods for real-world bladeicing detection.

WaveletAE: A Wavelet-enhanced Autoencoder for Wind Turbine Blade Icing Detection