Construction of Digital Twin Models for Giant Hydroelectric Units and Real-Time Data Synchronization Technology

Abstract

With the rapid development of China's hydropower industry, new problems faced by large-scale hydropower units are becoming increasingly prominent. The digitization and intelligent operation and maintenance of hydropower station equipment have become important ways to improve the operational efficiency of hydropower stations. Digital twin (DT) technology, as an emerging technological means, can play an important role in multiple applications of hydroelectric units. Establishing a real-time monitoring model for hydroelectric units and evaluating their health status has important practical significance. Based on this, this article explores the application concept of DT in the full life cycle management, fault diagnosis, and health management of hydropower units. Meanwhile, this article proposes a deep learning (DL) based DT model for giant hydroelectric units. This model utilizes DL technology to synchronously collect and analyze real-time data of hydroelectric units, thereby monitoring their operating status. The results show that the model proposed in this article can effectively synchronize real-time data of hydropower units and accurately warn potential faults.