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Novel integration graphical modeling method for the connection topology relationship of electrical equipment in distribution network

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Published:03 May 2024Publication History

ABSTRACT

Various power systems in the distribution network generate a large amount of power information data every day. And it is of great significance for power data monitoring to dynamically display them in a visualized way. Therefore, a visualization platform can display the status of the distribution network accurately. The related power information data can intuitively respond to the changes in the distribution network, so that the dispatchers can determine the operating status of the power grid quickly. Firstly, a data model is built for power equipment in this paper. And a topology model is built for connection relationship between power equipment according to common information model (CIM) to design the corresponding data structure and database. Secondly, a graphical element model is proposed for power equipment based on SVG technology to realize the electrical graphical element template applicable to distribution network. Thirdly, the connection relationship of distribution network is analyzed by topology analysis method. And a topology model is formed containing only topological nodes and topological islands. Finally, the topological analysis of the connection relationship of the distribution network is carried out by topological analysis method containing only topological nodes and topological islands. It is used to topology the connection relationship of the electrical equipment in the distribution network. The topological results are stored in the database for use in other dispatch functions.

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  1. Novel integration graphical modeling method for the connection topology relationship of electrical equipment in distribution network

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    • Published in

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      IoTAAI '23: Proceedings of the 2023 5th International Conference on Internet of Things, Automation and Artificial Intelligence
      November 2023
      902 pages
      ISBN:9798400716485
      DOI:10.1145/3653081

      Copyright © 2023 ACM

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      New York, NY, United States

      Publication History

      • Published: 3 May 2024

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