Anomaly detection for hydropower turbine unit based on variational modal decomposition and deep autoencoder

Abstract

Anomaly detection for hydropower turbine unit is a requirement for the safety of hydropower system. An unsupervised anomaly detection method employing variational modal decomposition (VMD) and deep autoencoder is proposed. VMD is employed to the data collected by multiple sensors to obtain the sub signal of each data. These sub signals in each time-period constitute two-dimensional data. The autoencoder based on convolutional neural network is used to complete unsupervised learning, and the reconstruction residual of autoencoder is used for anomaly detection. The experimental results show that the deep autoencoder can increase the interval between abnormal and normal data distribution, and VMD can effectively reduce the number of samples in the overlapping area. Compared with traditional autoencoder method, the proposed method improves the recall, precision and F1 scores by 0.140, 0.205 and 0.175, respectively. The proposed method achieves better anomaly detection performance than other methods.