A Fusion Model for Predicting the Vibration Trends of Hydropower Units

Hydropower units are essential to the safe, stable, and efficient operation of modern power systems, particularly given the current expansion of renewable energy systems. To enable timely monitoring of unit performance, it is critical to investigate the trends in vibration signals, to enhance the accuracy and reliability of vibration trend prediction models. This paper proposes a fusion model for the vibration signal trend prediction of hydropower units based on the waveform extension method empirical mode decomposition (W-EMD) and long short-term memory neural network (LSTMNN). The fusion model first employed a waveform matching extension method based on parameter ergodic optimization to extend the original signal. Secondly, EMD was used to decompose the extended signal sequence and reconstruct the decomposition components by the extreme point division method, and the reconstructed high- and low-frequency components were used as LSTMNN inputs for component prediction. Finally, the component prediction results were superimposed with equal weights to obtain the predicted value of the vibration signal trend of the hydropower unit. The experimental results showed that the W-EMD signal decomposition method can effectively suppress the endpoint effect problem in the traditional EMD algorithm, improving the quality of EMD decomposition. Furthermore, through a case study of the upper guide X direction swing signal on the 16F unit of a domestic hydropower station, it was found that the proposed fusion model successfully predicted anomalies in the unit’s swing signals; compared with SVR, KELM, LSTMNN, and EMD + LSTMNN, the prediction accuracy was improved by 78.94%, 66.67%, 55.56%, and 42.86%, respectively.