A Comparation Study of Transmission Line Routing Based on A* and RRT Algorithms
DOI:
https://doi.org/10.30564/jcsr.v6i3.6714Abstract
Transmission line design is an important part of power grid engineering design, and transmission line path selection is the basis of line design. Transmission line route selection is to select a line path that can not only meet the requirements of power transmission and consumption, but also consider the cost control of the line, and avoid the farmland, highways, ecological exclusion zone between the start and end of the transmission line. The path scheme is constrained by geographical factors, project cost, construction conditions, operation and maintenance conditions, which makes the path selection show the characteristics of comprehensive diversity, spatial complexity, policy influence and so on. In the traditional route selection of transmission lines, line design specialists need to go through blind route selection, site survey and other steps to determine the path scheme, but because of insufficient effectiveness of the map, it is difficult to reflect the real environmental information, resulting in repeated route selection work to determine the final path scheme. In this paper, the selection principle of transmission line path is introduced first. Then, two path optimization algorithms, i.e. A* algorithm and rapidly expanding random tree (RRT) algorithm, are used to select transmission lines respectively. Finally, the advantages and disadvantages of these two algorithms in practical engineering line selection design are comparatively analyzed. Compared with the way of A* algorithm, RRT algorithm is more suitable for practical application of power system. It aims to provide a reference for the method of intelligent line selection in subsequent transmission design.
Keywords:
A* algorithm; Rapidly expanding random tree (RRT) algorithm; Transmission line designReferences
[1] Zhao, J., Zhou, G., Hu, B., 2020. Application of geographic information technology in UHV DC transmission line project construction. Beijing Surveying and Mapping. 34(10), 1319–1324. (in Chinese)
[2] Ding, C., Bai, T., Wang, H., et al., 2020. Discussion on the deficiency and optimal path of transmission line operation and maintenance. China Plant Engineering. 23, 54–56. (in Chinese)
[3] Wang, J., 2021. Optimization method of 220kV transmission line based on artificial intelligence. Electric Engineering. 5, 129–132.
[4] Wang, F., 2021. Analysis and application of digital line selection technology for transmission Lines. Inner Mongolia Electric Power. 39(3), 92–94.
[5] Wang, Y., 2015. Optimization algorithm of transmission line route designing based on GIS [Master's Thesis]. North China Electric Power University.
[6] Deo N., Pang, C.,1984. Shortest-path algorithms: Taxono-my and annotation. Networks. 14(2), 275–323.
[7] Cherkassky, B.V., Goldberg, A.V., Radzik, T., 1996. Shortest-path algorithms: Theory and experimental evaluation. Mathematical Programming. 73, 129–174.
[8] Zhan, F.B., Noon, C.E.,1998. Shortest path algorithms: an evaluation using real road networks. Transportation Science. 32(1), 65–73.
[9] Wang, S., Wu, Z.,2012. Improved Dijkstra shortest path algorithm and its application. Computer Science. 39, 223–228.
[10] Wang, S., 2014. Multi-adjacency vertexes and multi-shortest-paths problem of Dijkstra algorithm. Computer Science. 217–224.
[11] Hu, L., Cao, Y., Geng, Y., et al., 2023. A comparative study of transmission routing decision optimization algorithm. In 2023 4th International Conference on Computer Science and Management Technology (ICCSMT 2023), October 13-15, 2023, Xi'an China. ACM, New York, NY, USA, 10 pages.
[12] Han, W., 2014. An improvement on fixed order Bellman-Ford algorithm. Journal of Harbin Institute of Technology. 46(11), 58–62.
[13] Xia, Z., Bu, T., Zhang, J., 2014. Analysis and improvement of SPFA algorithm. Computer Science. 41(6), 180–184.
[14] Zhao, W., Gong, Z., Wang, W., Fan, S., 2018.Comparative analysis of several classical shortest path algorithms. Journal of Chifeng University (National Science Edition). 34(12), 47–49. (in Chinese)
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