Cross region safety monitoring method of distribution secondary system network under Internet of things

Author:

Lin Liangcheng1,Xu Yonggang1,Zhang Yue1,Kang Chaoqun2,Sun Jian3

Affiliation:

1. State Grid Cyber Security Technology(Beijing) Co., Ltd., Beijing, China

2. State Grid Shanghai Energy Interconnection Research Institute Co., Ltd., Shanghai, China

3. State Grid Jiangsu Electric Power Co., Ltd., Research Institute, Nanjing Jiangsu, China

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.

Publisher

IOS Press

Subject

Artificial Intelligence,General Engineering,Statistics and Probability

Reference20 articles.

1. Grid Integration of Small-Scale Photovoltaic Systems in Secondary Distribution Network- A Review;Panigrahi;IEEE Transactions on Industry Applications,2020

2. Transient monitoring function-based islanding detection in power distribution network;Dubey;IET Generation, Transmission, Distribution,2019

3. A deep spatial-temporal data-driven approach considering microclimates for power system security assessment;Huang;Applied Energy,2019

4. Evaluation of HMM-Based Network Intrusion Detection System for Multiple Multi-Stage Attacks;Shawly;IEEE Network,2020

5. A Fully Data-Driven Method Based on Generative Adversarial Networks for Power System Dynamic Security Assessment With Missing Data;Ren;IEEE Transactions on Power Systems,2019

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Stealing complex network attack detection method considering security situation awareness;PLOS ONE;2024-03-21

2. Electrical Engineering Automation Power Supply and Distribution Safety Monitoring Method Under PLC;2023 3rd International Conference on Mobile Networks and Wireless Communications (ICMNWC);2023-12-04

3. Research on Evaluation System of Secondary Distribution Grid System;2023 Power Electronics and Power System Conference (PEPSC);2023-11-24

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3