Anomaly detection for hydropower turbine based on variational modal decomposition and hierarchical temporal memory

Abstract

Hydropower generation has become an important guarantee for social and economic evolution. The hydropower turbine is the main power device of hydropower generation. And the anomaly state of the turbine is important to the operation and maintenance of the turbine. The complex signals measured by the hydropower turbine sensor are decomposed into the sub signals employing variational modal decomposition (VMD). The accuracy of anomaly detection is improved by applying VMD. A hydropower turbine anomaly detection application combining VMD and hierarchical temporal memory (HTM) is developed. Specificities of spinning-tree, cluster, HTM and the proposed method are 0.730, 0.690, 0.820, 0.945, respectively. Accuracy are 0.872, 0.873, 0.931, 0.959, respectively, and F1 scores are 0.923, 0.924, 0.959, 0.976, respectively. The proposed method can be applied to other anomaly detection applications.