Transmission Line Fault Location Using PCA-Based Best-Fit Curve Analysis

被引:18
|
作者
Mukherjee A. [1 ]
Kundu P.K. [2 ]
Das A. [2 ]
机构
[1] Government College of Engineering and Ceramic Technology, Kolkata
[2] Department of Electrical Engineering, Jadavpur University, Kolkata
关键词
Curve fitting techniques; Least square; Principal component analysis (PCA); Principal component indices (PCI);
D O I
10.1007/s40031-020-00515-z
中图分类号
学科分类号
摘要
The paper presents a principal component analysis (PCA)-based method for localization of various power system faults in a 150 km long single side fed transmission line using quarter-cycle pre-fault and half-cycle post-fault sending end line current signals. The proposed work uses fault signals of ten different types of seven intermediate locations along the length of the line to develop three-phase PCA score indices. The localizer model is also designed for practical fitment, with fault signals contaminated with power system noise. These seven sets of indices are further used with the best-fit curve fitting method in the MATLAB environment to develop fault curves. Minimum root mean square error criteria are followed for selecting the fit type. Each fault class is designed with the required number of curves to estimate fault location. The proposed work produces a highly accurate localization, with only 0.1271% average percentage error for fault localization, and a maximum percentage error of 0.5821% for the 150 km line. © 2020, The Institution of Engineers (India).
引用
收藏
页码:339 / 350
页数:11
相关论文
共 50 条
  • [21] PCA-based on-line diagnosis of induction motor stator fault feed by PWM inverter
    Martins, J. F.
    Pires, V. Fernao
    Pires, A. J.
    2006 IEEE INTERNATIONAL SYMPOSIUM ON INDUSTRIAL ELECTRONICS, VOLS 1-7, 2006, : 2401 - +
  • [22] Analysis of Two Strategies in Skill Development Based on Resource Based View and Best-fit Model
    Huang Man
    HUMAN RESOURCES CHALLENGE DURING POST GFC PERIOD, 2011, : 118 - 122
  • [23] Fault Diagnosis for Power System Transmission Line Based on PCA and SVMs
    Guo, Yuanjun
    Li, Kang
    Liu, Xueqin
    INTELLIGENT COMPUTING FOR SUSTAINABLE ENERGY AND ENVIRONMENT, 2013, 355 : 524 - 532
  • [24] Fault diagnosis for power system transmission line based on PCA and SVMs
    Guo, Yuanjun
    Li, Kang
    Liu, Xueqin
    Communications in Computer and Information Science, 2013, 355 : 524 - 532
  • [25] Wind Power Converter Fault Diagnosis Using Reduced Kernel PCA-Based BiLSTM
    Attouri, Khadija
    Mansouri, Majdi
    Hajji, Mansour
    Kouadri, Abdelmalek
    Bouzrara, Kais
    Nounou, Hazem
    SUSTAINABILITY, 2023, 15 (04)
  • [26] Automated interpretation of PCA-based process monitoring and fault diagnosis using signed digraphs
    Vedam, H
    Venkatasubramanian, V
    ON-LINE FAULT DETECTION AND SUPERVISION IN THE CHEMICAL PROCESS INDUSTRIES 1998, 1998, : 219 - 223
  • [27] Fault location in transmission line using hybrid neural network
    Osowski, S
    Salat, R
    COMPEL-THE INTERNATIONAL JOURNAL FOR COMPUTATION AND MATHEMATICS IN ELECTRICAL AND ELECTRONIC ENGINEERING, 2002, 21 (01) : 18 - 30
  • [28] Study of Fault Location in Transmission Line Using S Transform
    Shang, Liqun
    Zhai, Wensong
    Liu, Pei
    2016 INTERNATIONAL SYMPOSIUM ON COMPUTER, CONSUMER AND CONTROL (IS3C), 2016, : 85 - 88
  • [29] Fault Location Estimation in HVDC Transmission Line Using ANN
    Johnson, Jenifer Mariam
    Yadav, Anamika
    PROCEEDINGS OF FIRST INTERNATIONAL CONFERENCE ON INFORMATION AND COMMUNICATION TECHNOLOGY FOR INTELLIGENT SYSTEMS: VOL 1, 2016, 50 : 205 - 211
  • [30] Transmission Line Fault Location Using Deep Learning Techniques
    Fan, Rui
    Yin, Tianzhixi
    Huang, Renke
    Lian, Jianming
    Wang, Shaobu
    2019 51ST NORTH AMERICAN POWER SYMPOSIUM (NAPS), 2019,