Development and Engineering Application of Fault Early Warning Model for Large Hydropower Turbine Based on Principal Component Analysis and Long Short-Term Memory Network

Abstract

With the expansion of hydropower stations and the complexity of operational environments, ensuring the safety of large hydroturbine units has become critical for hydropower station management. Current studies reveal challenges in intelligent monitoring and fault warning due to complex conditions and massive data, necessitating innovative methods for multidimensional data processing, risk identification, and accurate fault prediction. This study addresses the challenge of real-time operational data processing to enhance fault early warning accuracy for large hydroturbine units. Data is classified by monitoring locations and signal types, followed by governance using outlier analysis, KNN-based missing value imputation, and Bayesian-wavelet packet denoising. A PCA-LSTM fault early warning model is proposed, where principal component analysis (PCA) reduces dimensionality and extracts representative features, and long short-term memory (LSTM) captures time-series dependencies for fault prediction. Validation shows ${R}^{{2}}$ metrics of 0.9690.996 and MAE of 0.0480.502 for PCA1PCA6. Application to historical faults in A Hydropower Station demonstrates early warning times of 127304 time steps and failure warning times of 33170 time steps ahead of actual failures, highlighting the model’s timeliness.