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

Hao Luo; Li Cheng; Pengyong Yi; Jiuyi Wang; Xuetong Zhao; Lijun Yang; Ruijin Liao

doi:10.1049/hve2.70019

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

更新时间：2026-03-24

- Research on digital twin modelling technique for ±800 kV converter transformers scene based on hybrid attention mechanism and multiresolution hash encoding
- High Voltage Vol. 10, Issue 2, (2025)
- 作者机构：
  
  1. State Key Laboratory of Power Transmission Equipment Technology, School of Electrical Engineering, Chongqing University,Chongqing,China
  2. Tsinghua University Shenzhen International Graduate School,Shenzhen,China
- 作者简介：
- 基金信息：
- DOI：10.1049/hve2.70019
  CLC：
- Published：2025
- 稿件说明：
移动端阅览
Hao Luo, Li Cheng, Pengyong Yi, et al. Research on digital twin modelling technique for ±800 kV converter transformers scene based on hybrid attention mechanism and multiresolution hash encoding[J]. High Voltage, 2025, 10(2).
DOI：

Hao Luo, Li Cheng, Pengyong Yi, et al. Research on digital twin modelling technique for ±800 kV converter transformers scene based on hybrid attention mechanism and multiresolution hash encoding[J]. High Voltage, 2025, 10(2). DOI： 10.1049/hve2.70019.

摘要

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.

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

and the accuracy is improved by about 6%

realising the real-time modelling of transformers and the DT of scenes.

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