An Efficient Method for Transient Temperature Calculation in Oil Natural Transformers Based on the Time‐Space Proper Orthogonal Decomposition

A reduced‐order model (ROM) for the temperature field based on time‐space proper orthogonal decomposition (POD) is presented to improve the computational efficiency of transient temperature rise in oil‐immersed power transformers with a complete oil natural convection cooling loop. This approach utilises data obtained from a full‐order simulation model operating under the transformer's rated conditions as input. This paper first examines the reduction characteristics of the proper orthogonal decomposition (POD) method and subsequently introduces the time‐space POD framework. The time POD method is used to select a robust snapshot set, which is subsequently integrated with space POD and Galerkin methods to establish a rapid computation model for the transient temperature field prediction of the transformer under various loading conditions. Compared to traditional finite element models, this method can generate a highly accurate computational model with a small number of snapshot samples, significantly reducing the computation time required. The presented examples demonstrate the exceptional computational accuracy and efficiency of this method. Therefore, this approach offers a workable solution for the digital twin application of transformer temperature assessment by attaining second‐level computing efficiency for the entire transformer temperature field.