Stability and Safety Study of Pumped Storage Units Based on Time-Shifted Multi-Scale Cosine Similarity Entropy

The diagnosis of vibration signals of pumped storage units is crucial to the safe and stable operation of the units. In this paper, a fault diagnosis method with high recognition rate and strong generalizability is proposed. Firstly, the original vibration signals are decomposed by Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) decomposition, the decomposed IMF1 components are proposed to be decomposed by Variational Mode Decomposition (VMD) for secondary decomposition, and then based on the fractal theory, a tool to measure the signal complexity is proposed - Time-Shifted Multiscale Cosine Similarity Entropy (TSMCSE). Finally, the entropy value of each component is input into the Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) model as the feature value for diagnosis and identification. Through the measured data of unit operation, four states of 20 MW, 100 MW, 280 MW and 340 MW in the upper guide +X and -Y directions are used for validation, and the results show that the diagnostic accuracy of CEEMDAN-VMD-TSMCSECNN-LSTM reaches 100 %. At the same time, in order to experience the generalizability of the model, the open data set of rolling bearings from Jiangnan University is introduced, and four conditions in 600/rpm and 800 rpm are selected for re-validation, and the experimental results show that the accuracy of the model designed in this paper is 100 %. The method provides a new tool for the study of stability and safety of pumped storage units.