Recent developments in data-driven and hybrid intelligent techniques for hydropower fault analysis

Abstract

Hydropower systems operate under complex hydraulic, mechanical, and electrical conditions, and their operational reliability is closely related to equipment safety and maintenance efficiency. With the increasing availability of monitoring data, intelligent algorithms have been gradually introduced into hydropower fault prediction and condition assessment. This review summarizes recent developments in machine learning, deep learning, physics-informed neural networks, and digital twin-assisted approaches for hydropower fault analysis. Representative studies and typical applications are discussed, with attention given to their diagnostic performance, data dependence, and applicability under different operating conditions. Existing studies indicate that conventional machine learning methods still perform effectively in limited-sample scenarios, while deep learning models are more suitable for extracting complex features from multi-source monitoring signals and time-series data. Hybrid approaches combining physical mechanisms with data-driven analysis have shown potential for improving model robustness and reliability. In addition, digital twin frameworks provide a possible way to integrate real-time monitoring, fault diagnosis, and operational assessment within a unified platform. Despite recent progress, several challenges remain, including limited fault data, model interpretability, and differences in operating conditions among hydropower stations. Future studies are expected to place greater emphasis on multi-source data fusion, improved model adaptability, and the integration of physical knowledge with intelligent algorithms, supporting more reliable fault analysis and condition monitoring in hydropower systems.