杨帆, 吴涛, 廖瑞金, 江金洋, 陈涛, 高兵. 数字孪生在电力装备领域中的应用与实现方法[J]. 高电压技术, 2021, 47(5): 1505-1521. DOI: 10.13336/j.1003-6520.hve.20210456
引用本文: 杨帆, 吴涛, 廖瑞金, 江金洋, 陈涛, 高兵. 数字孪生在电力装备领域中的应用与实现方法[J]. 高电压技术, 2021, 47(5): 1505-1521. DOI: 10.13336/j.1003-6520.hve.20210456
YANG Fan, WU Tao, LIAO Ruijin, JIANG Jinyang, CHEN Tao, GAO Bing. Application and Implementation Method of Digital Twin in Electric Equipment[J]. High Voltage Engineering, 2021, 47(5): 1505-1521. DOI: 10.13336/j.1003-6520.hve.20210456
Citation: YANG Fan, WU Tao, LIAO Ruijin, JIANG Jinyang, CHEN Tao, GAO Bing. Application and Implementation Method of Digital Twin in Electric Equipment[J]. High Voltage Engineering, 2021, 47(5): 1505-1521. DOI: 10.13336/j.1003-6520.hve.20210456

数字孪生在电力装备领域中的应用与实现方法

Application and Implementation Method of Digital Twin in Electric Equipment

  • 摘要: 数字孪生(digital twin,DT)是推动电力装备领域数字化、智能化发展的关键技术之一,相关研究处于初期起步阶段,如何实现电力装备数字孪生成为亟需解决的问题。该文首先阐述了数字孪生的内涵及应用;从数字孪生框架与实现方法、其在装备全寿命周期的应用、主流厂商及其平台方面总结了装备领域数字孪生的研究与应用进展;其次分析了电力装备数字孪生实现所需的关键技术;最后以变压器为例给出了基于Microsoft Azure和ANSYS Twin Builder的电力装备多物理场数字孪生实现方法,并指出实现电力装备数字孪生在数据采集、模型构建与求解、平台使用方面的挑战。该文建议下一步开发高性能传感装置并构建合理的传感网络提升数据采集的深度与广度;开展电力装备全尺度多物理场模型的构建与实时求解算法研究;开发面向电力装备性能分析的国产化数字孪生平台。

     

    Abstract: Digital twin is the key technology to promote the development of digitization and intelligence in the field of electric equipment. The relevant research is in initial stage, and how to achieve digital twin of electric equipment is an urgent problem to be solved. This paper describes the intension and applications of digital twin and then summarizes the research and application progress of digital twin in the field of equipment from the aspects of digital twin framework and implementation method, application in equipment life cycle, mainstream manufacturers and their platforms. Secondly, the key technologies of digital twin in the field of electric equipment are analyzed. Finally, an electric transformer is taken as an example, and the implementation method of the electric equipment multiphysics digital twin based on the Microsoft Azure and ANSYS Twin Builder is given. This paper also points out the challenges faced by the digital twin of electric equipment in data collection, model building and solution, and platform use. The recommendations are as follows: developing high-performance sensors and building a reasonable sensor network to enhance the depth and breadth of data collection; developing a full-scale multi-physical field model and real-time solving algorithm for electric equipment; and developing a national digital twin platform for electric equipment performance analysis.

     

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