A digital twin framework for real-time monitoring and intelligent operation of small hydropower plants

Small hydropower plants (SHPs) are essential for renewable energy generation and regional water management. Conventional monitoring and operational approaches often suffer from limited real-time data integration, insufficient predictive capability and delayed response to disturbances, leading to reduced energy efficiency and increased mechanical fatigue. To address these issues, this study presents a digital twin framework for SHPs, integrating real-time sensing, hybrid modelling and intelligent control. The system combines physics-based and data-driven models, with sensor networks collecting hydrological, meteorological and equipment status data processed via a cloud-edge architecture. Machine learning algorithms predict streamflow, turbine performance and potential faults, while a model fusion strategy reconciles physical and data-driven predictions for accurate state estimation and operational decision support. The framework was deployed at a pilot SHP, achieving 5–7% mean absolute error in streamflow and power prediction over 6 months. Real-time monitoring and optimised control increased overall energy efficiency by 3–5% and maintained continuous ecological flow compliance. The proposed digital twin approach demonstrates strong potential for scalable and intelligent management of SHPs, enhancing predictive capability, operational efficiency and environmental sustainability.