@Article{dedt.2024.048142,
AUTHOR = {Yao Jin, Jie Zhao, Xiaozhe Tan, Linghou Miao, Wenxing Yu},
TITLE = {Research on Substation Siting Based on a 3D GIS Platform and an Improved BP Neural Network},
JOURNAL = {Digital Engineering and Digital Twin},
VOLUME = {2},
YEAR = {2024},
NUMBER = {1},
PAGES = {131--144},
URL = {http://www.techscience.com/dedt/v2n1/59198},
ISSN = {},
ABSTRACT = {Substation siting is an important foundation and a key task in power system planning. The article is based on a three-dimensional GIS platform combined with an improved BP neural network algorithm and proposes a substation siting method that is more efficient, accurate and provides a better user experience. Firstly, the BP algorithm is enhanced to improve its convergence speed and computational efficiency for a more accurate and reasonable calculation of optimal site selection. Then, a 24-item selection index system with 7 categories is proposed, which provides quantifiable data support and an evaluation basis for substation site selection. Finally, based on the 3G GIS platform, combined with the improved BP algorithm, site selection evaluation indicators, and multi-source data automatically extracted by the platform, the application research of substation site selection was carried out. The experimental simulation results show that the method proposed in the paper has better robustness and accuracy compared with traditional methods such as PCA and AHP. Compared to traditional manual site selection, site selection based on a 3D GIS platform has a better intuitive experience and convenience, which improves the efficiency of site selection and the user experience level.},
DOI = {10.32604/dedt.2024.048142}
}