Vibration Forecasting of Pumped Storage Units Base on EMD-KPCA-LSTM
In the vibration trend prediction of pumped storage units,the conventional prediction methods are difficult to predict accurately due to the highly nonlinear and non-stationary vibration signal time series.In this paper,a vibration prediction model of pumped storage unit is proposed,which combines empirical mode decomposition(EMD),kernel principal component analysis(KPCA)improved by principal component analysis(PCA)and long short-term memory neural network(LSTM).The model uses EMD algorithm to decompose vibration signals,and the KPCA is used to screen out the key influencing factors.Finally,the LSTM is used to carry out time-dynamic modeling of feature sequences to realize vibration prediction of pumped storage units.Compared with the traditional LSTM,EMD-LSTM and other pre-diction models,the experimental results show that this model has better prediction effect and can predict the change trend of vibration signals more accurately.
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