Abstract:In response to the global surge in energy demand and the transition towards green energy, small hydropower stations in mountainous regions play a pivotal role in local green energy provision, and their safe operation is of great importance. However, these stations face high operational risks due to their geographical remoteness and difficult maintenance.. To address the imperative for intelligent monitoring and early warning systems at these stations, this study introduces an advanced technology grounded on power communication networks. By establishing a hierarchical system architecture, we facilitate real-time collection, transmission, processing, and analysis of data from hydropower stations. Leveraging machine learning and ensemble learning techniques, we design an intelligent monitoring and early warning algorithm. Following data preprocessing, feature extraction, model training, and parameter optimization, there is a marked enhancement in prediction accuracy and model generalizability. Experimental outcomes indicate that our system proficiently oversees key parameters of hydropower stations, offering timely alerts during anomalies, thereby exhibiting commendable stability and reliability. Furthermore, the system empowers operation and maintenance managers to intuitively perceive the status of hydropower stations and promptly address risks through visualization. This research furnishes technical backing for the safe and efficient functioning of small hydropower stations in mountainous terrains and offers insights for future endeavors in intelligent monitoring and early warning systems for such installations.