Prediction of vibration trend of hydroelectric unit based on OVMD-BIGRU

Predicting the oscillation evolution of hydraulic turbines is crucial to ensuring their safe and stable operation. However, accurately and effectively predicting the oscillation evolution of hydraulic turbines can be challenging due to their non-stationary and nonlinear characteristics. Therefore, this article proposes an oscillation evolution prediction model for hydroelectric units based on Optimal variational modal decomposition-Bilateral gated recurrent neural networks (OVMD-BIGRU). Firstly, the decomposition parameters of Optimal variational modal decomposition (OVMD) are determined using the central frequency observation method and the residual index minimization criterion, avoiding subjective factors. Then perform Variational modal decomposition (VMD), normalize each modal component, and establish a BIGRU model for prediction. Finally, reverse normalize and stack the modal results to obtain the final predicted vibration trend of the unit. This article designs a comparative experiment based on the unit data of a domestic hydraulic power station, and the findings indicate that the proposed model has good performance and high prediction accuracy.