Research on intelligent operation and maintenance of power systems in manufacturing enterprises based on digital twin technology

With the rapid development of industrial manufacturing, the operation and maintenance problems of the power system are becoming increasingly prominent, and traditional manual maintenance methods are no longer able to meet the needs. This article aims to provide better power support for manufacturing enterprises. This article establishes a digital twin model of the power system, which corresponds the physical information and operational data of the actual power system to the digital model. Based on this model for simulation and prediction, machine learning and artificial intelligence algorithms are used to analyze historical data, extract features and patterns, and establish a monitoring and prediction model for the operation status of the power system. Finally, through real-time monitoring and prediction, timely detection and handling of faults and anomalies in the power system, the results can effectively improve the operational efficiency and reliability of the power system in manufacturing enterprises. Through real-time monitoring and prediction, faults and anomalies in the power system can be detected in a timely manner, reducing downtime and maintenance costs. Digital twin technology can also provide analysis and optimization suggestions for operating data for manufacturing enterprises, helping them improve production efficiency and competitiveness.