The valve-side bushing of a converter transformer is a critical component in ultra-high-voltage direct current (UHVDC) systems, making its monitoring through digital twin technology highly significant. However, the complex structure and spatio-temporal nonlinearity of the bushing result in a large computational demand for its digital twin model, which requires an effective order reduction algorithm. This paper proposes a Spatio-temporal Non-uniformity Proper Orthogonal Decomposition (SN-POD) algorithm considering the inhomogeneity of space and time consumption to meet the reduced-order computational requirements of UHV valve-side bushings. This proposed method reduces the calculation time to 10% of the full-order simulation model while controlling the error range of the key research area less than 0.1%. The test results show that this method has good robustness, calculation speed, and accuracy. This research can significantly enhance the computational efficiency of digital twin modeling for valve-side bushings and provide a technical foundation for constructing digital twin models for UHV valve-side bushings.

To address the inverse identification of contact-related thermal faults in gas-insulated switchgear (GIS), this study proposes a method for contact resistance inversion and internal temperature field reconstruction. The proposed method enables the estimation of faulty internal contact resistance using external enclosure temperature data, while simultaneously reconstructing the internal temperature field. First, a forward numerical model of GIS is established, and a POD-Kriging surrogate model is developed to achieve second-level rapid prediction of the forward problem. Based on this surrogate model, the thermal fault inversion problem is formulated as an optimization problem of fault parameters and solved using the Grey Wolf Optimizer. GIS temperature-rise experiments are performed to validate the numerical model, and a real GIS contact fault case is further analyzed. The results indicate that the proposed method yields an average inversion error of 9.5% for degraded contact resistance, with the maximum error at internal temperature monitoring points remaining below 8%. The total inversion time is approximately 30 s. These findings demonstrate that the proposed method is capable of effective online inversion and diagnosis of contact-related thermal faults in GIS equipment.

In the context of new-type power system construction, digital twin has become the core technology for power transformers, supporting their full-life cycle intelligent operation and maintenance. The real-time, high-precision calculation of the internal temperature field serves as the core supporting element for realizing the real-time mapping between the physical transformer entity and its virtual twin. Aiming at the inherent defects of traditional temperature rise calculation methods, such as insufficient accuracy and an excessively long computation time, this paper proposes a simplified calculation model for the transformer temperature field. In this model, the transformer oil tank is simplified into a two-dimensional axisymmetric thermal–fluid coupled field model solved by the finite volume method (FVM). The Proper Orthogonal Decomposition (POD) technique is adopted to perform order reduction on the matrices involved in the governing equations, so as to reduce the computational degrees of freedom. Meanwhile, the radiator is equivalent to a one-dimensional thermal circuit model, and the field–circuit coupled solution is achieved through bidirectional data mapping. Temperature field calculation is carried out for a 220 kV oil-immersed transformer based on the proposed model. The results show that the average relative error between the calculated results and the experimental data is around 0.86%, while the computation time is merely 0.04% of that of the traditional three-dimensional full-scale model. Furthermore, taking the real-time overload capacity evaluation of the transformer as a case, it is verified that the proposed model can successfully support the requirements of practical engineering applications.