Water Resources and Power

To accurately obtain the health performance level of a pumped storage unit(PSU), a health performance tendency prediction method based on convolution neural network-long short-term memory neural network(CNN-LSTM) is proposed. Firstly, a unit health state model based on Gaussian process regression was constructed to effectively characterize the operating characteristics of the PSU. Then, an index that can quantify the health performance of the PSU was proposed. Finally, by integrating the good local feature extraction ability of the CNN and the advantage of the LSTM in time series prediction, a prediction model based on CNN-LSTM was proposed. The experiments were conducted using monitoring data from a pumped storage station in China. The results show that the proposed method can betterly predict the future evolution of the PSU's health performance.