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...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2025-04-01
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| Series: | High Voltage |
| Online Access: | https://doi.org/10.1049/hve2.70019 |
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| _version_ | 1849713183357403136 |
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| author | Hao Luo Li Cheng Pengyong Yi Jiuyi Wang Xuetong Zhao Lijun Yang Ruijin Liao |
| author_facet | Hao Luo Li Cheng Pengyong Yi Jiuyi Wang Xuetong Zhao Lijun Yang Ruijin Liao |
| author_sort | Hao Luo |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-e5d03e2beccf421b892cdd7edce1fead |
| institution | DOAJ |
| issn | 2397-7264 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Wiley |
| record_format | Article |
| series | High Voltage |
| spelling | doaj-art-e5d03e2beccf421b892cdd7edce1fead2025-08-20T03:14:01ZengWileyHigh Voltage2397-72642025-04-0110229430410.1049/hve2.70019Research on digital twin modelling technique for ±800 kV converter transformers scene based on hybrid attention mechanism and multiresolution hash encodingHao Luo0Li Cheng1Pengyong Yi2Jiuyi Wang3Xuetong Zhao4Lijun Yang5Ruijin Liao6State Key Laboratory of Power Transmission Equipment Technology School of Electrical Engineering Chongqing University Chongqing ChinaState Key Laboratory of Power Transmission Equipment Technology School of Electrical Engineering Chongqing University Chongqing ChinaTsinghua University Shenzhen International Graduate School Shenzhen ChinaState Key Laboratory of Power Transmission Equipment Technology School of Electrical Engineering Chongqing University Chongqing ChinaState Key Laboratory of Power Transmission Equipment Technology School of Electrical Engineering Chongqing University Chongqing ChinaState Key Laboratory of Power Transmission Equipment Technology School of Electrical Engineering Chongqing University Chongqing ChinaState Key Laboratory of Power Transmission Equipment Technology School of Electrical Engineering Chongqing University Chongqing ChinaAbstract 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.https://doi.org/10.1049/hve2.70019 |
| spellingShingle | Hao Luo Li Cheng Pengyong Yi Jiuyi Wang Xuetong Zhao Lijun Yang Ruijin Liao Research on digital twin modelling technique for ±800 kV converter transformers scene based on hybrid attention mechanism and multiresolution hash encoding High Voltage |
| title | Research on digital twin modelling technique for ±800 kV converter transformers scene based on hybrid attention mechanism and multiresolution hash encoding |
| title_full | Research on digital twin modelling technique for ±800 kV converter transformers scene based on hybrid attention mechanism and multiresolution hash encoding |
| title_fullStr | Research on digital twin modelling technique for ±800 kV converter transformers scene based on hybrid attention mechanism and multiresolution hash encoding |
| title_full_unstemmed | Research on digital twin modelling technique for ±800 kV converter transformers scene based on hybrid attention mechanism and multiresolution hash encoding |
| title_short | Research on digital twin modelling technique for ±800 kV converter transformers scene based on hybrid attention mechanism and multiresolution hash encoding |
| title_sort | research on digital twin modelling technique for 800 kv converter transformers scene based on hybrid attention mechanism and multiresolution hash encoding |
| url | https://doi.org/10.1049/hve2.70019 |
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