Research on Fault Warning and Maintenance Strategies for Automated Production Equipment in Smart Power Plants Based on Digital Twins

With the deepening intelligent transformation of the energy generation industry, the stable operation of automated production equipment in smart power plants has become increasingly critical to ensuring the continuity of energy supply. Currently, traditional fault warning models suffer from issues such as delayed response and low data utilization efficiency. These shortcomings make them inadequate for meeting the operation and maintenance (O&M) demands of high-parameter, large-capacity power generation equipment. This inadequacy frequently leads to unplanned outages, severely constraining improvements in generation efficiency and operational cost control. In response, this study focuses on the application potential of digital twin technology in the O&M of power generation equipment. It aims to establish a comprehensive system for fault warning and maintenance strategies that integrates physical-virtual mapping, multi-source data integration, and intelligent decision-making. The research not only provides theoretical foundation and technical references for O&M practices in smart power plants but also holds practical significance for advancing the transition of the energy generation industry towards more efficient and intelligent O&M models.