  Wind power, as an alternative to burning fossil fuels, is abundant and
inexhaustible. To fully utilize wind power, wind farms are usually located in
areas of high altitude and facing serious ice conditions, which can lead to
serious consequences. Quick detection of blade ice accretion is crucial for the
maintenance of wind farms. Unlike traditional methods of installing expensive
physical detectors on wind blades, data-driven approaches are increasingly
popular for inspecting the wind turbine failures. In this work, we propose a
wavelet enhanced autoencoder model (WaveletAE) to identify wind turbine
dysfunction by analyzing the multivariate time series monitored by the SCADA
system. WaveletAE is enhanced with wavelet detail coefficients to enforce the
autoencoder to capture information from multiple scales, and the CNN-LSTM
architecture is applied to learn channel-wise and temporal-wise relations. The
empirical study shows that the proposed model outperforms other
state-of-the-art time series anomaly detection methods for real-world blade
icing detection.
