Application of Digital Twin Model Based on Parameter Encoding in the Calibration of UHVDC Transmission System

The calibration of ultra-high voltage direct current (UHVDC) transmission systems, as an advanced electric power system that transmits electric energy with high efficiency and over long distances, is crucial to ensure the safe and stable operation of the system. However, due to the complexity and high technical requirements, it faces difficulties in calibration accuracy, calibration methods, and equipment. For this reason, this article proposes a digital twin model containing a physical system, data interaction, and a digital system, which adopts a two-layer feature extraction structure to recognize key devices through parameter perception. Then, the feature information of key devices is parametrically identified and the relative topological features are learned. Experimental results show that mean squared error, root mean squared error, and mean absolute error indexes are reduced by 71.08%, 46.32%, and 64.39%, respectively, and the R2 score is improved by 3.8% compared with the original method. It provides an important guarantee for the safe operation and reliability of the UHVDC system.