Abstract
The production and operation of distribution network in China involves cross link and cross specialty efficient collaboration. The data types of each business link of distribution network are complex and diverse, and the presentation forms are different. The attributes of each distribution equipment node are heterogeneous, and the interaction between each equipment is very frequent. In the continuous development of the new active distribution network, the value of data expression and data mining is further increased, and more and more data resources are integrated in various business systems and platforms of the distribution network. The accelerated formation of the new distribution network intensifies this evolution trend. The degree of sharing and value realization of data resources determines the height of business development and management improvement of the distribution network. The internal structure of the distribution network is complex, and there are many kinds of power equipment involved, so there are huge difficulties in data monitoring. The existing state monitoring methods have very high error rate in practical application, and the response time is long, which can not meet the actual needs. This paper proposes a rapid construction method of digital twins of distribution network based on data drive, which provides a reference for the operation state monitoring of power equipment.
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References
Tang, L., Tian, F., Lu. X., et al.: Distribution network digital twin evaluation system oriented to “shared intelligence”. China Electr Power (11), 2 (2020)
Du, X., Wang, L., Zhao, J., et al.: Research on implementation and application of digital twin technology in distribution network substation. Hebei Electr. Power Technol. 40(3), 6 (2021)
Wang, R., Ding, J., Wang, Y., et al.: Research on data reliability of distribution switch cabinet temperature sensor based on digital twin. Power Big Data 024(006), 27–35 (2021)
Tang, X., Yao, J., Wan, H., et al.: Construction of digital twin application platform for regional multi energy system. Electron. Technol. Appl. 48(1), 8 (2022)
He, X., Ai, Q., Zhu, T., et al.: Opportunities and challenges of digital twins in power system applications. Power Grid Technol. 44(6), 11 (2020)
Du, X., Zeng, S., Liu, K., et al.: Digital twin construction method of distribution network operation portrait based on cloud model. Power Syst Protect Control (2022)
Liu, H., Shao, J., Wang, X., et al.: State evaluation and fault prediction of distribution automation terminal equipment based on digital twins. Power Grid Technol (004), 046 (2022)
Tang, Q., Chen, B., Deng, W., et al.: Research on application of digital twin technology in AC/DC distribution network. Guangdong Electr Power (2020)
Wang, R., Ding, J., Wang, Y., et al.: Research on data reliability of distribution switch cabinet temperature sensor based on digital twin. Power Big Data 24(6), 9 (2021)
Zhang, Z., Peng, J., Sun, S., et al.: Design and implementation of digital twin management platform for power grid. Hebei Electr Power Technol 41(1), 8–11 (2022)
Acknowledgements
This work was supported by State Grid Corporation of China’s Science and Technology Project (5108-202218280A-2-396-XG) which is ‘Research on rapid construction and linkage analysis technology of distribution network facilities digital twins integrated with electrical topology’.
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Qiao, J. et al. (2024). Research and Implementation of Rapid Construction of Digital Twins in Distribution Network Based on Data Driven. In: Kountchev, R., Patnaik, S., Nakamatsu, K., Kountcheva, R. (eds) Proceedings of International Conference on Artificial Intelligence and Communication Technologies (ICAICT 2023). ICAICT 2023. Smart Innovation, Systems and Technologies, vol 369. Springer, Singapore. https://doi.org/10.1007/978-981-99-6956-2_6
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DOI: https://doi.org/10.1007/978-981-99-6956-2_6
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