Research on forecasting method of hydropower unit deterioration based on EEMD and LSTM

As a low-speed rotating equipment, the hydroelectric generator has a complicated operating mechanism. In the absence of prior knowledge and few fault samples, it is difficult to make a correct judgment on the operating status of a hydroelectric generator using traditional fault diagnosis methods. In view of the above problems, a method for predicting the degradation degree of hydroelectric generators based on the combination of ensemble empirical mode decomposition (EEMD) and long short-term memory (LSTM) is proposed. Using the data of the hydroelectric generator during non-failure operation to calculate the standard of the health value of the characteristic parameters under different working conditions, using the degree of degradation to describe the degree to which the characteristic value deviates from the health value during the operation of the generator. Furthermore, the EEMD method is used to decompose the original non-stationary degradation time series into several stationary component sequences. Finally, the LSTM prediction algorithm is used to predict the deterioration degree of the generator. The prediction results show that the method has good prediction accuracy and can accurately predict the deterioration trend of hydroelectric generators. © 2022, Solar Energy Periodical Office Co., Ltd. All right reserved.