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Article type: Research Article
Authors: Lin, Liangchenga | Xu, Yongganga | Zhang, Yuea; * | Kang, Chaoqunb | Sun, Jianc
Affiliations: [a] State Grid Cyber Security Technology(Beijing) Co., Ltd., Beijing, China | [b] State Grid Shanghai Energy Interconnection Research Institute Co., Ltd., Shanghai, China | [c] State Grid Jiangsu Electric Power Co., Ltd., Research Institute, Nanjing Jiangsu, China
Correspondence: [*] Corresponding author. Yue Zhang, State Grid Cyber Security Technology(Beijing) Co., Ltd., Beijing 102209, China. E-mail: [email protected].
Abstract: In order to ensure the safe transmission of the information of the secondary distribution system across the regional network, this paper studies a security monitoring method of the secondary distribution system across the regional network based on the Internet of things technology and the improved fuzzy clustering algorithm. The Internet of things technology is used to collect the information transmission in cross region network of the secondary power distribution system and store it in the database; Combined with the shadow set to improve the basic fuzzy C-means clustering algorithm, the improved fuzzy C-means clustering algorithm is obtained. The cross region information transmission in the clustering database is divided into two categories: security and risk, and the risk information obtained by clustering is divided into four risk types, so as to realize the security monitoring of information transmission in cross region network of secondary power distribution system. The results show that the average monitoring rate of this method can reach 93.93%, the information collection is efficient and accurate, the number of packet losses is low, and the clustering results are stable and reliable, which can ensure the safe information transmission of cross region network of the secondary power distribution system.
Keywords: Internet of things technology, fuzzy clustering algorithm, secondary power distribution system, network cross region transmission, security monitoring, cluster analysis
DOI: 10.3233/JIFS-221154
Journal: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7807-7819, 2022
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