Application of Digital Twin in Health Status Assessment of Giant Hydropower Units

In recent years, artificial intelligence (AI) has achieved good results in the field of device status detection and has gradually become an important application in water conservancy engineering projects. The proposal of the concept of “smart water conservancy” has promoted the application of new technologies in water conservancy engineering to achieve more efficient and intelligent water conservancy management goals. In this context, digital twin (DT) technology, as an emerging technology, provides new possibilities for the multi domain application of hydropower units, especially in digital modeling and intelligent monitoring, showing great potential. Faced with the increasingly complex operation and maintenance requirements and challenges of giant hydropower units, this article innovatively proposes a health status (HS) assessment system based on DT technology and combined with deep learning (DL). This system, through the DL algorithm, can not only accurately evaluate the performance of hydroelectric units, but also quantitatively evaluate health indicators, thereby achieving comprehensive monitoring of unit status. The results show that the system performs well in fault diagnosis, with accuracy fully meeting practical application requirements and effectively addressing the limitations of traditional methods in fault diagnosis.