Anomaly detection for wind turbines based on the reconstruction of condition parameters using stacked denoising autoencoders

Abstract

This paper proposes an approach for detecting anomalies in a wind turbine (WT) based on multivariate analysis. Firstly, the stacked denoising autoencoders (SDAE) model with moving window and multiple noise levels is developed to reconstruct the normal operating data. The correlations among multivariable and temporal dependency inherent in each variable can be captured simultaneously with moving window processing. Both the coarse-grained and fine-grained features of input data can be learned by training with multiple noise levels. Then, the monitoring indicator is derived from the reconstruction error. The threshold value of monitoring indicator is determined by statistical analysis of the values of the monitoring indicator during normal operation. To identify the most relevant parameter related to the detected anomaly in WT, the contribution degree to which each parameter contributes to the exceedance of the threshold is calculated. Finally, the abnormal level is quantified according to the overlap between test behavior distribution and baseline condition to provide supports for operation and maintenance planning of WT. Demonstration on real SCADA data collected from a wind farm in Eastern China shows that the proposed method is effective for the anomaly detection and early warning of an actual WT.