A 3D parametric modeling method for the grid information model

Shutao Dong; Zhibin Li; Chunlin Li; Xiangshang Wang; Jianguang Zhang; Jianuo Wang

doi:10.1117/12.3019389

23 January 2024 A 3D parametric modeling method for the grid information model

Shutao Dong, Zhibin Li, Chunlin Li, Xiangshang Wang, Jianguang Zhang, Jianuo Wang

Proceedings Volume 12978, Fourth International Conference on Geology, Mapping, and Remote Sensing (ICGMRS 2023); 129782W (2024) https://doi.org/10.1117/12.3019389
Event: 2023 4th International Conference on Geology, Mapping and Remote Sensing (ICGMRS 2023), 2023, wuhan, China

Abstract

In order to meet the needs of information and intelligent service of power transmission and transformation project, the power industry proposed the grid information model (GIM), which is a digital information model of the whole life cycle of power grid components and is of great significance for realizing information sharing in power industry. On this basis, State Grid has formulated a unified GIM model specification, but the specification only gives the parameters of the fundamental geometry units, and does not involve the specific implementation. The existing platform cannot build the model directly according to the given parameters. And for that, this paper proposes a 3D parametric modeling method for GIM, establishes a hierarchical tree structure based on the fundamental geometry units in the specification, and proposes a GIM fundamental geometry units modeling algorithm applicable to this structure, according to which the automatic construction of GIM fundamental geometry units can be realized. Finally, the feasibility and effectiveness of the method proposed in this paper are verified by experiments.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Shutao Dong, Zhibin Li, Chunlin Li, Xiangshang Wang, Jianguang Zhang, and Jianuo Wang "A 3D parametric modeling method for the grid information model", Proc. SPIE 12978, Fourth International Conference on Geology, Mapping, and Remote Sensing (ICGMRS 2023), 129782W (23 January 2024); https://doi.org/10.1117/12.3019389

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