Research on Real-Time Monitoring and Fault Prediction of Digital Twin Power Station Driven by Computer Vision

This paper focuses on the application of computer vision technology in real-time monitoring and fault prediction of digital twin power stations, and proposes an innovative operation and maintenance system architecture integrating Light GBM algorithm. The architecture captures real-time data through a high-precision sensor network and combines 3D modeling technology to create a digital twin of the substation, seamlessly connecting the physical world with the digital world. The system adopts the edge computing strategy to process the massive data initially, reduce the load of the central server, and ensure the real-time data. Through in-depth analysis of the historical operation data of the power station, the Light GBM algorithm can identify potential failure modes, and dynamically adjust them through real-time monitoring data, so as to achieve accurate prediction of equipment status. By simulating the state of the power station under different operating conditions, the system designed in this paper shows excellent adaptability and robustness. It effectively improves the safety and reliability of the power station.