Integrated Intelligent Energy ›› 2024, Vol. 46 ›› Issue (1): 65-74.doi: 10.3969/j.issn.2097-0706.2024.01.008

• Cyber-Physical Security • Previous Articles     Next Articles

Application and prospect of multimodal knowledge graph in electric power operation inspection

LIN Jiajun1(), YAN Weidan2,*(), HU Junhua3, ZHENG Yiming1, SHAO Xianjun1, GUO Bingyan2   

  1. 1. State Grid Zhejiang Electric Power Company Electric Power Research Institute,Hangzhou 310011,China
    2. College of Electrical Engineering, Zhejiang University,Hangzhou 310027,China
    3. State Grid Zhejiang Electric Power Company,Hangzhou 310007,China
  • Received:2022-09-27 Revised:2023-04-03 Online:2024-01-25 Published:2023-05-05
  • Supported by:
    Science and Technology Project of State Grid Corporation of China(5700-202019487A-0-0-00)

Abstract:

In the context of building a new power system with new energy as the main body,knowledge graph(KG),a large-scale visual semantic network,is expanding its applications rapidly in power operation and inspection. The applications of KG in power operation and inspection mainly focus on semantic information processing. However,a large amount of heterogeneous data will be generated in power grid operation,being able to uphold the construction of multimodal knowledge graph(MMKG) which provides data support for various downstream tasks. In view of functional requirements on electric power inspection, MMKG is introduced to support the intelligent query answering system and fault handling. Expounding the construction technology of MMKG for power inspection data,the scenarios of power operation and inspection that MMKG can give full play in are summarized,and the development direction is forecasted. Finally,the challenges that will be faced by MMKG is analyzed comprehensively,which provides a reference for the development of intelligent power operation and inspection.

Key words: new power system, knowledge graph, multimodality, power operation and inspection, fault handling, artificial intelligence, big data

CLC Number: