In order to solve the problems of difficulty in characterizing the deteriorated state of hydropower units and low prediction accuracy,it is necessary to deeply explore the distribution difference characteristics of operating efficiency under different states.This paper presents a deteriorated state trend prediction of hydropower units based on the differ-ence of operating efficiency distribution.Firstly,considering the mapping relationship between the operating conditions(head and flow)and efficiency of hydropower units,and the randomness of the state monitoring data,Gaussian mixture model is used to fit the probability distribution characteristics of the units operating efficiency under multiple operating conditions.On this basis,the negative log-likelihood probability of the observed samples under the units health state dis-tribution is calculated,which is used as an index of the deteriorated state of the hydropower units to characterize the devi-ation between the observed samples and the standard distribution of the units health state.Furthermore,the expansion convolution module and the residual module of the time convolution network are improved respectively by using the non-causal principle and the Gaussian error linear units,and the gate recurrent units is fused to design and build the deteriora-ted state prediction model of the hydropower units.Finally,the proposed method is verified by using the actual monito-ring data of Unit#6 in a hydropower station.The results show that the proposed method can effectively improve the trend prediction accuracy of deteriorated state.
关键词
水电机组/机组效率/劣化状态指标/趋势预测/时间卷积网络/门控循环单元
Key words
hydropower units/efficiency of units/index of deteriorated state/trend prediction/time convolutional network/gate recurrent units
维普
万方数据