Online detection method for variable load conditions and anomalous sound of hydro turbines using correlation analysis and PCA-adaptive-K-means

Abstract

The hydro turbine is the critical component of hydropower stations. Sound analysis provides a versatile non-contact approach for detecting anomalies or faults of hydro turbines. However, sound analysis methods such as Fourier transform and AI-based pattern recognition algorithms have limitations in achieving online detection due to potential fault frequency aliasing, non-obvious fault characteristics, lack of anomalous samples, and irregular occurrence of anomalous sounds. To overcome these challenges, the proposed method initially employs online correlation analysis to distinguish variable load conditions, followed by the detection of anomalous sounds under stable load conditions using principal component analysis (PCA) and adaptive K-means clustering (K-means). In the method, time–frequency analysis and wavelet packet decomposition are incorporated to extract signal features, with PCA employed to highlight anomalous features. Subsequently, feature correlation analysis and online adaptive clustering are used to accurately detect variable load conditions and anomalous sounds. The proposed method does not rely on prior knowledge of anomalous data, making it flexible and applicable to most practical hydro turbines. Additionally, the method aims to establish a robust foundation for fault diagnosis utilizing sound signals in hydro turbines, providing real-time detection of variable load conditions or anomalous sounds every second. To validate the effectiveness of the proposed method, detection experiments are conducted in practical hydropower station.