Automatic identification of transmission sections based on complex network theory

被引:26
|
作者
Luo Gang [1 ]
Shi Dongyuan [1 ]
Chen Jinfu [1 ,2 ]
Duan Xianzhong [1 ]
机构
[1] Huazhong Univ Sci & Technol, State Key Lab Adv Electromagnet Engn & Technol, Sch Elect & Elect Engn, Wuhan 430074, Peoples R China
[2] Washington State Univ, Sch Elect Engn & Comp Sci, Pullman, WA 99164 USA
基金
中国国家自然科学基金;
关键词
POWER; VULNERABILITY;
D O I
10.1049/iet-gtd.2013.0466
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Taking transmission sections as the monitoring objects of power system security and stability level can largely improve the efficiency of analysis and control in power system operation. However, existing approaches for identifying transmission sections mainly depend on years of experience, which is not suitable for complicated and variable power systems with large scales. Thus, a novel method for automatic identification of transmission sections using complex network theory is proposed. The proposed method presents the fundamental conditions of transmission sections and identifies them from three levels: transmission lines, key transmission links and partition sections. First, based on the small-world characteristics of power grid, the index of transmission betweenness is presented to identify key transmission links from transmission lines. Then clustering algorithm of complex network is used to divide the power grid and to obtain partition sections from the key transmission links. Finally, the combinations of partition sections are selected and ranked as the transmission sections. Numerical results with CEPRI-36 system and a provincial system are provided to demonstrate the effectiveness and adaptability of the proposed method.
引用
收藏
页码:1203 / 1210
页数:8
相关论文
共 50 条
  • [1] Power transmission network vulnerable region identifying based on complex network theory
    Zhao, Hongshan
    Zhang, Chao
    Ren, Hui
    2008 THIRD INTERNATIONAL CONFERENCE ON ELECTRIC UTILITY DEREGULATION AND RESTRUCTURING AND POWER TECHNOLOGIES, VOLS 1-6, 2008, : 1082 - 1085
  • [2] Identification of Key Nodes in Aircraft State Network Based on Complex Network Theory
    Wang Zekun
    Wen Xiangxi
    Wu Minggong
    IEEE ACCESS, 2019, 7 : 60957 - 60967
  • [3] Research on the Structural Characteristics of Transmission Grid Based on Complex Network Theory
    Zhao, Jinli
    Zhou, Hongshan
    Chen, Bo
    Li, Peng
    JOURNAL OF APPLIED MATHEMATICS, 2014,
  • [4] Identification of vulnerable lines in power grid based on complex network theory
    Chen, Xiaogang
    Sun, Ke
    Cao, Yijia
    Wang, Shaobu
    2007 IEEE POWER ENGINEERING SOCIETY GENERAL MEETING, VOLS 1-10, 2007, : 1699 - 1704
  • [5] Identification of Key Flight Conflict Nodes Based on Complex Network Theory
    Wu M.
    Wang Z.
    Gan X.
    Yang G.
    Wen X.
    Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University, 2020, 38 (02): : 279 - 287
  • [6] Identification of transmission sections based on power grid partitioning
    Nan, Lu
    Liu, Tianqi
    He, Chuan
    INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS, 2019, 29 (04)
  • [7] A novel transmission line vulnerability evaluation method based on complex network theory
    Du Zhi
    Wang Gang
    You Dahai
    Chen Weihua
    Wang Ke
    Zou Yang
    CURRENT DEVELOPMENT OF MECHANICAL ENGINEERING AND ENERGY, PTS 1 AND 2, 2014, 494-495 : 1866 - +
  • [8] A vulnerable points identification method based on complex network theory and an operation index
    Xie L.
    Li Y.
    Luo L.
    Zeng X.
    Cao Y.
    Dianli Xitong Baohu yu Kongzhi/Power System Protection and Control, 2022, 50 (04): : 83 - 91
  • [9] Risk identification of major infectious disease epidemics based on complex network theory
    Fu, Lingmei
    Yang, Qing
    Liu, Zheng
    Liu, Xingxing
    Wang, Zhan
    INTERNATIONAL JOURNAL OF DISASTER RISK REDUCTION, 2022, 78
  • [10] A condition assessment model of EHV/UHV transmission line based on bayesian network and complex network theory
    Jiang, Le
    Liu, Junyong
    Wei, Zhenbo
    Gong, Hui
    Huang, Yuan
    Li, Chengxin
    Gaodianya Jishu/High Voltage Engineering, 2015, 41 (04): : 1278 - 1284