Abstract
In order to improve the reliability of distribution network, a grid planning method of distribution network based on artificial intelligence technology is studied. Firstly, according to the principle of grid division, the planning area is divided into several planning grids reasonably; the existing problems of the existing distribution network are analyzed systematically, and the weak links of the existing distribution network are summarized. According to the current grid structure, load forecasting results and planning objectives of planning grid, the target grid and transition grid of planning grid are determined, and the load forecasting method of distribution network is designed to improve the scientificity of distribution grid division. The experiments show that after the grid planning of the distribution network in a planning area, the target grid of the distribution network is strong, the power supply range is clear, and the power supply reliability is high, which verifies the effectiveness and scientificity of the method.
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References
Tabrizi, B.H., Rabbani, M.: A graph theoretic-based approach to distribution network planning with routes interaction regarding the fix-charge transportation problem. Int. J. Oper. Res. 38(1), 112–136 (2020)
Huang, Y, Zhuang, X., Liu, H., et al.: Application of power load forecasting in urban distribution network planning based on 3d real scene platform. J. Phys. Conf. Ser. 1549(5), 052121 (2020)
Zhang, L., Tang, W., Liang, J., et al.: Coordinated day-ahead reactive power dispatch in distribution network based on real power forecast errors. IEEE Trans. Power Syst. 31(3), 2472–2480 (2019)
Popovic, Z.N., Kovacki, N.V., Popovic, D.S.: Resilient distribution network planning under the severe windstorms using a risk-based approach. Reliab. Eng. Syst. Saf. 204(01), 107–114 (2020)
Muhammad, M.A., Mokhlis, H., Naidu, K., et al.: Distribution network planning enhancement via network reconfiguration and DG integration using dataset approach and water cycle algorithm. J. Mod. Power Syst. Clean Energy 8(1), 86–93 (2020)
Popovic, Z.N., Knezevic, S.D., Kerleta, V.D.: Network automation planning in distribution networks with distributed generators using a risk-based approach. Electr. Eng. 101(2), 659–673 (2019)
Li, Z., Wu, W., Zhang, B., et al.: Hexagon raster-based method for distribution network planning considering line routes and pole locations. IET Gener. Transm. Distrib. 14(8), 1420–1429 (2020)
Fu, W., Liu, S., Srivastava, G.: Optimization of big data scheduling in social networks. Entropy 21(9), 902 (2019)
Liu, S., Lu, M., Li, H., et al.: Prediction of gene expression patterns with generalized linear regression model. Front. Genet. 10, 120 (2019)
Liu, S., Liu, D., Srivastava, G., Połap, D., Woźniak, M.: Overview and methods of correlation filter algorithms in object tracking. Complex Intell. Syst., 1–23 (2020). https://doi.org/10.1007/s40747-020-00161-4
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Guan-hua, F., Da-xing, C., Yang, S., Jia, X., Fei-feng, W., Lian-huan, Z. (2021). Research on Grid Planning Method of Distribution Network Based on Artificial Intelligence Technology. In: Fu, W., Xu, Y., Wang, SH., Zhang, Y. (eds) Multimedia Technology and Enhanced Learning. ICMTEL 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 387. Springer, Cham. https://doi.org/10.1007/978-3-030-82562-1_8
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DOI: https://doi.org/10.1007/978-3-030-82562-1_8
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