A Generalized Admittance Based Method for Fault Location Analysis of Distribution Systems

被引:0
|
作者
Tan, Zhenyu [1 ]
Sun, Hongbo [2 ]
Nikovski, Daniel [2 ]
Takano, Tomihiro [3 ]
Kojima, Yasuhiro [3 ]
Ohno, Tetsufumi [3 ]
机构
[1] Georgia Inst Technol, Atlanta, GA 30329 USA
[2] Mitsubishi Elect Res Labs, Cambridge, MA 02139 USA
[3] Mitsubishi Electr Corp, Kobe, Hyogo 6618661, Japan
关键词
Distribution system; equivalent admittance matrix; fault location analysis; short circuit fault;
D O I
暂无
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
This paper proposes a generalized admittance based method for fault location analysis of distribution systems. Based on the measurements collected from the feeder breakers and intelligent switches during a fault, the fault type and faulted feeder section are first determined by examining the over-voltages and over-currents on the breakers/switches. The load demands, faulted line segment and fault location are then determined sequentially by finding a set of loads, a line segment and a fault location that has minimal distance between the currents measured at the boundaries of the feeder section, and the estimated currents determined by multiplying the measured voltages by an equivalent admittance matrix determined for the feeder section when applying the given load and fault conditions to the feeder section. The proposed approach determines the equivalent admittance matrix for a feeder section with one or two measuring ports through topology and circuit analysis, and for a feeder section with more than two measuring ports through Kron reduction on nodal admittance matrix. Numerical examples are given to demonstrate the effectiveness of the proposed approach.
引用
收藏
页数:5
相关论文
共 50 条
  • [41] Novel Fault-Location Method for Overhead Electrical Distribution Systems
    Alwash, Shamam Fadhil
    Ramachandaramurthy, Vigna Kumaran
    IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING, 2013, 8 : S13 - S19
  • [42] A fuzzy set method for fault location identification in power distribution systems
    Liu, WHE
    Zhong, WL
    PROCEEDINGS OF THE 35TH IEEE CONFERENCE ON DECISION AND CONTROL, VOLS 1-4, 1996, : 2208 - 2212
  • [43] A New Fault-Location Method with High Robustness for Distribution Systems
    You, Dahai
    Ye, Lei
    Yin, Xianggen
    Yao, Qiguo
    Wang, Ke
    Wu, Junchun
    ELEKTRONIKA IR ELEKTROTECHNIKA, 2013, 19 (06) : 31 - 36
  • [44] TRANSFORMER FAULT DIAGNOSIS AND LOCATION METHOD BASED ON FAULT TREE ANALYSIS
    Wu, Zhiwu
    Huang, Tianfu
    Wang, Chunguang
    Wu, Xiang
    Tu, Yanzhao
    SCALABLE COMPUTING-PRACTICE AND EXPERIENCE, 2024, 25 (05): : 3587 - 3593
  • [45] Analysis on Distribution Network Fault Location Method Based on Parallel Resistance Disturbed Signal Injection
    Qi Z.
    Zhuang S.
    Liu Z.
    Yao Z.
    Zhang Y.
    Dianli Xitong Zidonghua/Automation of Electric Power Systems, 2018, 42 (09): : 195 - 200
  • [46] Line fault location in a distribution network based on K-fault diagnosis method
    Peng, MF
    He, YG
    Tan, Y
    Huang, HL
    Sheng, XY
    2003 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS, INTELLIGENT SYSTEMS AND SIGNAL PROCESSING, VOLS 1 AND 2, PROCEEDINGS, 2003, : 571 - 575
  • [47] A fault segment location method for distribution networks based on spiking neural P systems and Bayesian estimation
    Wang, Yi
    Wang, Tao
    Liu, Liyuan
    PROTECTION AND CONTROL OF MODERN POWER SYSTEMS, 2023, 8 (01)
  • [48] A machine learning based fault location method for power distribution systems using wavelet scattering networks
    Arsoniadis, Charalampos G.
    Nikolaidis, Vassilis C.
    SUSTAINABLE ENERGY GRIDS & NETWORKS, 2024, 40
  • [49] A fault segment location method for distribution networks based on spiking neural P systems and Bayesian estimation
    Yi Wang
    Tao Wang
    Liyuan Liu
    Protection and Control of Modern Power Systems, 2023, 8
  • [50] Comparison of measurement features used as inputs in a learning based fault location method for power distribution systems
    Correa-Tapasco, Ever
    Mora-Florez, Juan
    Perez-Londono, Sandra M.
    UIS INGENIERIAS, 2019, 18 (01): : 73 - 79