Abnormal vibration detection of wind turbine based on temporal convolution network and multivariate coefficient of variation. (15th July 2022) - Zhan, Jun; Wu, Chengkun; Ma, Xiandong; Yang, Canqun; Miao, Qiucheng; Wang, Shilin
Highlights: High-resolution (1 Hz) SCADA data with both time and frequency domain information being considered. A framework based on the spectrum-embedded temporal convolutional network (SETCN) is applied to extract latent features. Multi-variate coefficient of variation (MCV) is utilized to fuse vibration residual and environmental parameters and construct anomaly assessment index. Results with data collected from several real-world wind farms demonstrate the effectiveness of the proposed approach. Abstract: A working wind turbine generates a large amount of multivariate time-series data, which contain abundant operation state information and can predict impending anomalies. The anomaly detection of the wind turbine nacelle that houses all of the generating components in a turbine have been challenging due to its inherent complexities, systematic oscillations and noise. To address these problems, this paper proposes an unsupervised time-series anomaly detection approach, which combines deep learning with multi-parameter relative variability detection. A normal behavior model (NBM) of nacelle vibration is firstly built upon training normal historical data of the supervisory control and data acquisition (SCADA) system in the high-resolution domain. To better capture the temporal characteristics and frequency information of vibration signals, the vibration spectrum vector is integrated with the multivariate time-series data as inputs and the spectrum-embedded temporal convolutional network (SETCN) is then used to extract latent features. The anomalies are detected through a multi-variate coefficient of variation (MCV) based anomaly assessment index (AAI) of relative variability among vibration residuals and environment parameters of the nacelle. The approach considers the time-series characteristics of input data and preserves the spatio-temporal correlation between variables. Validations using data collected from real-world wind farms demonstrate the effectiveness of the proposed approach.