WaveletAE: A Wavelet-enhanced Autoencoder for Wind Turbine Blade Icing Detection
This work addresses blade icing detection for wind farm maintenance, offering a data-driven alternative to expensive physical detectors, but it appears incremental as it builds on existing autoencoder and time series anomaly detection techniques.
The authors tackled wind turbine blade icing detection by proposing WaveletAE, a wavelet-enhanced autoencoder that analyzes SCADA system time series, and it outperformed other state-of-the-art methods in empirical studies.
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.