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- Title
Anomaly Detection of Wind Turbine Driveline Based on Sequence Decomposition Interactive Network.
- Authors
Lyu, Qiucheng; He, Yuwei; Wu, Shijing; Li, Deng; Wang, Xiaosun
- Abstract
Aimed at identifying the health state of wind turbines (WTs) accurately by using the comprehensive spatio and temporal information from the supervisory control and data acquisition (SCADA) data, a novel anomaly-detection method called decomposed sequence interactive network (DSI-Net) is proposed in this paper. Firstly, a DSI-Net model is trained using preprocessed data from a healthy state. Subsequences of trend and seasonality are obtained by DSI-Net, which can dig out underlying features both in spatio and temporal dimensions through the interactive learning process. Subsequently, the trained model processes the online data and calculates the residual between true values and predicted values. To identify anomalies of the WTs, the residual and root mean square error (RMSE) are calculated and processed by exponential weighted moving average (EWMA). The proposed method is validated to be more effective than the existing models according to the control experiments.
- Subjects
INTRUSION detection systems (Computer security); ANOMALY detection (Computer security); STANDARD deviations; ONLINE data processing; INTERACTIVE learning; SUPERVISORY control systems; WIND turbines
- Publication
Sensors (14248220), 2023, Vol 23, Issue 21, p8964
- ISSN
1424-8220
- Publication type
Article
- DOI
10.3390/s23218964