Intelligent line selection of power transmission and transformation project supported by geographic information technology

Li Wang; Ke Yang; Zhen LI; Wei Wang; Xuelin Li

doi:10.1117/12.3007052

25 October 2023 Intelligent line selection of power transmission and transformation project supported by geographic information technology

Li Wang, Ke Yang, Zhen LI, Wei Wang, Xuelin Li

Author Affiliations +

Proceedings Volume 12801, Ninth International Conference on Mechanical Engineering, Materials, and Automation Technology (MMEAT 2023); 1280156 (2023) https://doi.org/10.1117/12.3007052
Event: Ninth International Conference on Mechanical Engineering, Materials, and Automation Technology (MMEAT 2023), 2023, Dalian, China

Abstract

In order to solve the problems of blind repetition, inefficiency and error prone in line selection of power transmission and transformation projects, various influencing factors of line selection and their quantitative methods are studied, and a variable resolution grid data model is designed. combined with terrain analysis and spatial analysis in geographic information technology, a shortest path route selection algorithm considering many factors is proposed. The method presented in this paper is compared with the traditional method of manual drawing line selection. The experimental analysis shows that the correct rate of the intelligent line selection in this paper is more than 95.82%, which meets the requirements of the project. And the speed of line selection is much faster than that of traditional line selection. It provides an intelligent, fast and feasible method for line selection of power transmission and transformation projects.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Li Wang, Ke Yang, Zhen LI, Wei Wang, and Xuelin Li "Intelligent line selection of power transmission and transformation project supported by geographic information technology", Proc. SPIE 12801, Ninth International Conference on Mechanical Engineering, Materials, and Automation Technology (MMEAT 2023), 1280156 (25 October 2023); https://doi.org/10.1117/12.3007052

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