Abstract
The science and technology and society in China are gradually developing, and the demand for electrical energy is gradually increasing, and there are more and more domestic smart grid constructions. The abnormality of the grid cable has a direct impact on the power supply. In order to better improve the detection capability for abnormal fault of grid cable, the automatic detection technology is performed based on the cable nodes in smart grid. It is an identification based on the abnormal spectrum existing in the current transmission of power grid, and intelligent extraction is carried out, so that the abnormal fault nodes of each cable can be automatically detected, and the fusion of abnormal fault of cable nodes is established. The author first analyzes the faults of cable in smart grid, then analyzes the simulation experiment, and finally applies the automatic detection technology to the abnormal faults of cable nodes in smart grid.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Zhang, L.: Application research of fault diagnosis technology for power cable in smart grid. Sci. Technol. Innov. 28 (2019)
Alemu, S.: NetsaNet. Application of soft computing technology in microgrid automation: renewable energy generation forecasting, demand forecasting, microgrid protection (2019)
Zhang, J.: Research on key technologies of performance-oriented network attack detection system for intelligent substations based on CPPS fusion (2019)
Juan, F., Hongjie, Z., Jiafeng, S., et al.: Deep-level detection technology for mass operation data of smart grid based on regulation and control cloud. Autom. Instrum. 230(12), 168–171 (2018)
Wang, C., Zhang, X., Xie, L.: Research on temperature, humidity and dangerous environment monitoring of cable trenches in large substations based on intelligent sensor identification technology. In: 2018 Smart Grid Informatization Construction Seminar (2018)
Gao, Q., Liu, Q., Guo, Z., et al.: Research on preprocessing and recognition technology of partial discharge signals in GIS withstand voltage test based on ultrasonic signals. In: Proceedings of the 3rd Smart Grid Conference (2018)
Linqian, H., Minghui, W.: Automatic detection and analysis of appearance defects of smart meters based on image processing. J. Taiyuan Univ. Nat. Sci. Edn. 36(03), 50–55 (2018)
Wenchao, S.: Decision analysis of fault diagnosis and repair for cable nodes in smart grid based on AHP. Chin. Sci. Technol. Expo 37, 297 (2015)
Liu, T., Guan, X., Liu, Y., et al.: Research on detection method of smart grid attack based on data fusion of physical system and information network anomaly
Guo, J., Han, X., Li, S., et al.: Research on optimization scheme of primary cable laying in substation based on three-dimensional design. In: 2018 Smart Grid Informatization Construction Seminar (2018)
Acknowledgements
The project of 2017 Guangzhou Panyu District Innovation Leading Team--The R&D and Industrialization of The Intelligent Power Grid Transmission and Distribution Line Connection Products (2017-R01–7).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Hu, Q., Huang, Y., Xu, C., Li, S., Zou, H. (2021). Analysis of Automatic Detection Technology for Abnormal Faults of Cable Nodes in Smart Grid. In: Atiquzzaman, M., Yen, N., Xu, Z. (eds) Big Data Analytics for Cyber-Physical System in Smart City. BDCPS 2020. Advances in Intelligent Systems and Computing, vol 1303. Springer, Singapore. https://doi.org/10.1007/978-981-33-4572-0_98
Download citation
DOI: https://doi.org/10.1007/978-981-33-4572-0_98
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-33-4573-7
Online ISBN: 978-981-33-4572-0
eBook Packages: Computer ScienceComputer Science (R0)