Research on digital twin modelling technique for ±800 kV converter transformers scene based on hybrid attention mechanism and multiresolution hash encoding

Abstract Digital twin (DT) modelling is a prerequisite for the successful application of DT technology in the power industry. However, traditional scene modelling methods are costly, time‐consuming, focus on overall features and lack real‐time updates, hindering the interaction between DT models and physical power equipment scenes. Therefore, a scene DT modelling technique focusing on local features in risk areas and real‐time updates is urgently needed. Herein, real‐time modelling of the ±800 kV converter transformer is achieved by improving the neural radiation field based on a hybrid attention mechanism and multiresolution hash encoding. Compared to traditional methods, modelling time is reduced from hours to 1 min without professional equipment or manual intervention. The model quality is more concerned with local features of risk areas in transformers while ensuring the overall scene, and the accuracy is improved by about 6%, realising the real‐time modelling of transformers and the DT of scenes.