Real-Time Monitoring and Fault Diagnosis Method of Distribution Network State Based on Digital Twinning Technology

As the terminal link of power system, the running state of distribution network directly affects the quality of power supply and service level. With the acceleration of urbanization and the growth of power demand, the distribution network faces the challenges of complex network structure and huge number of equipment, and the requirements of users for power supply reliability and power quality are constantly improving. This paper presents a real-time monitoring and fault diagnosis method of distribution network based on digital twinning technology, aiming at improving the operation efficiency and reliability of distribution network. This method realizes the real-time interaction and mapping between the physical system and the digital system by constructing the virtual mirror image of the physical world, and provides data support and decision-making basis for the operation and maintenance management of the distribution network. In this paper, the hierarchical modeling method is used to divide the distribution network into substation layer, feeder layer and equipment layer, collect the operation data of power equipment and preprocess it to ensure the data quality. The hierarchical distribution network model is established by using object-oriented design method, and the equipment data is collected in real time through the Internet of Things technology and uploaded to the central monitoring platform to dynamically update the state and parameters in the model. Fault diagnosis and early warning identifies anomalies and preliminarily judges fault types by comparing real-time monitoring data with normal operation data of equipment, and uses machine learning algorithm to classify and evaluate faults according to extracted features. The experimental results show that this method can accurately and real-timely monitor the operation state of distribution network, and quickly diagnose and locate faults when they occur, which improves the efficiency and accuracy of fault handling and shows obvious advantages compared with traditional methods.