Integrated deep learning models to predict future vibrations on the discharge ring of a river-type hydroelectric power plant

Abstract

Hydroelectric power plants (HPPs) are critical for sustainable energy generation, but their maintenance and operational stability are often compromised by structural vibrations, particularly in key components like the discharge ring units. Predicting these vibrations in advance is essential to prevent damage, enhance operational efficiency, and extend the lifespan of HPP components. This paper presents two advanced deep learning models designed to predict future vibrations in the discharge ring of river-type HPPs. By combining multiple deep learning architectures, the proposed models process complex sensor data to accurately predict vibration patterns. The models employ the hybrid compositions of deep learning models specifically optimized for time-series prediction of mechanical stresses. In this study, vibration patterns of five distinct HPP turbine units (TUs) are modeled with a hybrid approach and comprehensive analyses are provided for each TU. Validation of the developed models with real-world operating data from HPPs reveals the proposed models’ accuracy, resilience, and potential for predicting future vibration signals. The proposed models achieve significant improvement in predictive accuracy over traditional methods, providing a reliable tool for early detection of vibration-induced risks in hydroelectric power infrastructure. The proposed models achieved minimum error rates with mean absolute error (MAE) of 0.025, mean squared error (MSE) of 0.006, R2 of 0.999 and root mean squared error (RMSE) of 0.080 for convolutional neural network + bidirectional long-short term memory (CNN + BiLSTM) and MAE of 0.038, MSE of 0.008, R2 of 0.994 and RMSE of 0.089 for CNN + gated recurrent unit. This study contributes to advancing predictive maintenance in HPPs and offers a scalable solution for enhancing the safety and resilience of renewable energy facilities.