To address the insufficient digitalization and intelligence of pumped storage units during operation,as well as the long computation time of existing simulation software that fails to meet real‑time requirements for digital twin systems,this study proposes a digital twin‑based solution to enhance monitoring and prediction capabilities for pump‑turbines.A reduced‑order model was established using proper orthogonal decomposition (POD) and integrated into the system via the FMI protocol,achieving visual interaction through Open3D point cloud with the entire process taking only 728.6 milliseconds.Simultaneously,a pressure pulsation prediction model based on long short‑term memory (LSTM) networks was developed for operational condition forecasting,with prediction accuracy exceeding 96%.Additionally,a complete digital twin system was developed using the Unity3D platform and validated for effectiveness and feasibility.This research provides a novel solution for the digitalization and intelligence of pumped storage units,holding significant implications for the intelligent upgrade and modern transformation of the hydropower industry.
| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |