Research on State Trend Prediction of Hydraulic Turbine Units Based on Mult-dimensional and Multi-condition Data

Abstract

Aiming at the problem that it is difficult to accurately predict the flexible operation conditions and state trend of hydraulic turbine, in this paper a cascade model of BP-LSTM classification prediction is proposed, which can identify the working conditions of existing fusion data, and then predict the measuring points of different working conditions. Based on the pressure parameters of hydraulic turbine units, the improved BP neural network is used to determine the operation conditions of hydraulic turbine units, and the classified data is redivided to establish the multivariate LSTM prediction model. By optimizing the parameters of the multivariate LSTM prediction model, such as the structure, the number of network layers and the number of hidden layer neurons, finally established the cascade model of BP-LSTM classification prediction of time series of hydraulic turbine units. Through experimental verification and analysis, BP-LSTM classification prediction model can predict the operation trend of measuring points under different working conditions after classification. Compared with other models, BP-LSTM model has higher prediction accuracy and better effect. The cascade model of BP-LSTM classification prediction of time series provides a model basis for the research of predictive control of hydraulic turbine units.