Working Condition Analysis and State Trend Prediction of Hydraulic Turbine Units

Based on the pressure parameters of the turbine, the operation condition o f the turbine is determined. Based on the input data, the operation condition o f the turbine is predicted by the long short term memory network . Firstly, the identification model of BP neural network method is established to identify the specif ic working c o nditions by using the historical values obtained in the practical eng ineering application . Then, according to the correlation between the measuring points, the multiple time series long short term memory network prediction model (LSTM) is cons tructed, and the state trend of the hydraulic turbine unit under this con dition is predicted. The corresponding punishment factors are calculated by using the p rediction data of each measuring point and the threshold value of the prediction band, which are mapped into the radar chart Finally, an anomaly early warning system wi th flexible early warning rules based on equipment deviation index is proposed. Through the experimental analysis, the validity of the long short term memory network prediction model and the rada r graph model for calculating the deviation degree o f the equipment is verified, and the advanced warning for the abnormal state of different acquisition points under different working conditions is realized, which provides a new method for the abnormal pr e diction and fault diagnosis of the hydraulic turbin e. KCI Citation Count: 0