Trend Prediction of Vibration Signals for Pumped-Storage Units Based on BA-VMD and LSTM

Nan Hu, Linghua Kong, Hongyong Zheng, Xulei Zhou, Jian Wang, Jian Tao, Weijiao Li and Jianyi Lin ()
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Nan Hu: Fujian Xianyou Pumped Storage Power Co., Ltd., Putian 351267, China
Linghua Kong: Fujian Xianyou Pumped Storage Power Co., Ltd., Putian 351267, China
Hongyong Zheng: Fujian Xianyou Pumped Storage Power Co., Ltd., Putian 351267, China
Xulei Zhou: Fujian Xianyou Pumped Storage Power Co., Ltd., Putian 351267, China
Jian Wang: Fujian Xianyou Pumped Storage Power Co., Ltd., Putian 351267, China
Jian Tao: Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China
Weijiao Li: Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China
Jianyi Lin: Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China

Energies, 2024, vol. 17, issue 21, 1-18

Abstract: Under “dual-carbon” goals and rapid renewable energy growth, increasing start-stop frequency poses new challenges to safe operations of pumped-storage power plant equipment. Ensuring equipment safety and predictive maintenance under complex conditions urgently requires vibration warnings and trend forecasting for pumped-storage units. In this study, the measured vibration-signal characteristics of pumped-storage units in a strong background-noise environment are obtained using a noise-reduction method that integrates BA-VMD and wavelet thresholding. We monitored the vibration-signal data of hydroelectric units over a long period of time, and the measured vibration-signal characteristics of pumped-storage units in a strong background-noise environment are accurately obtained using a noise-reduction method that integrates BA-VMD and wavelet thresholding. In this paper, a BP neural network prediction model, a support vector machine (SVM) prediction model, a convolutional neural network (CNN) prediction model, and a long short-term memory network (LSTM) prediction model are used to predict the trend of vibration signals of the pumped-storage unit under different operating conditions. The model prediction effect is analyzed by using the different error evaluation functions, and the prediction results are compared with the predicted results of the four different methods. By comparing the prediction effects of the four different methods, it is concluded that LSTM has higher prediction accuracy and can predict the vibration trends of hydropower units more accurately.

Keywords: pumped storage; trend prediction; vibration warning; support vector machine; convolutional neural network; long short-term memory network (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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