A survey on intelligent system application to fault diagnosis in electric power system transmission lines

被引:83
|
作者
Ferreira, V. H. [1 ]
Zanghi, R. [2 ]
Fortes, M. Z. [1 ]
Sotelo, G. G. [1 ]
Silva, R. B. M. [1 ]
Souza, J. C. S. [1 ]
Guimaraes, C. H. C. [1 ]
Gomes, S., Jr. [1 ]
机构
[1] Univ Fed Fluminense, Sch Engn, Niteroi, RJ, Brazil
[2] Univ Fed Fluminense, Inst Comp, Niteroi, RJ, Brazil
关键词
Intelligent systems; Power systems; Fault diagnosis; Transmission lines; WAVELET MULTIRESOLUTION ANALYSIS; SUPPORT VECTOR MACHINE; OF-THE-ART; ARTIFICIAL NEURAL-NETWORK; EXTREME LEARNING-MACHINE; LOCATION ALGORITHM; EXPERT-SYSTEM; DETECTION/LOCATION TECHNIQUE; SECTION ESTIMATION; ARCING FAULT;
D O I
10.1016/j.epsr.2016.02.002
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
Fault analysis and diagnosis constitute a relevant problem in power systems, with important economic impacts for operators, maintenance agents and the power industry in general. This has motivated the research and development of new algorithms and methods to address this problem. Intelligent systems have been proposed in the literature to deal with this problem in a significant number of applications. In the context of fault diagnosis in electric power systems, this survey presents a review of intelligent systems application to fault diagnosis in electric power system transmission lines. A huge number of related works can be found in the literature, being the major contributions reported in international journals. Then, the works cited in the present survey are restricted to those reported in regular journals that present high adherence to the aforementioned subject. The classification of strategies employed and their relationships with classical techniques are presented and discussed, allowing the identification of the main trends and research areas related to transmission line intelligent fault diagnosis systems. (C) 2016 Elsevier B.V. All rights reserved.
引用
收藏
页码:135 / 153
页数:19
相关论文
共 50 条
  • [41] Application of Wavelet Neural Network to Fault Diagnosis of Power System
    Hu Jian
    Hu Fanjun
    PROCEEDINGS OF THE 2015 INTERNATIONAL CONFERENCE ON AUTOMATION, MECHANICAL CONTROL AND COMPUTATIONAL ENGINEERING, 2015, 124 : 2283 - 2288
  • [42] Application of Artificial Intelligence in Power System Monitoring and Fault Diagnosis
    Wang, Guang
    Xie, Jiale
    Wang, Shunli
    ENERGIES, 2023, 16 (14)
  • [43] 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
  • [44] 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
  • [45] A survey on application of swarm intelligence computation to electric power system
    Bai, Hua
    Zhao, Bo
    WCICA 2006: SIXTH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION, VOLS 1-12, CONFERENCE PROCEEDINGS, 2006, : 7587 - 7591
  • [46] Fault Location on Power System Transmission Lines using Synchronized and Unsychronized Data
    Ndamase, Sindiswa
    Awodele, Kehinde
    Tetteh, Bright
    2021 IEEE PES/IAS POWERAFRICA CONFERENCE, 2021, : 224 - 228
  • [47] A hybrid intelligent system for fault diagnosis of advanced manufacturing system
    Ye, N
    Zhao, B
    INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH, 1996, 34 (02) : 555 - 576
  • [48] Experimental Application on a Mechanical Transmission System of Integrated Fault Diagnosis and Fault Prognosis method
    Benmoussa, S.
    Djeziri, M. A.
    IFAC PAPERSONLINE, 2018, 51 (24): : 1016 - 1023
  • [49] Electric Transmission System Fault Identification Using Modular Artificial Neural Networks for Single Transmission Lines
    Asbery, C. W.
    Liao, Y.
    2020 CLEMSON UNIVERSITY POWER SYSTEMS CONFERENCE (PSC), 2020,
  • [50] Structure and model of a fault intelligent diagnosis system for power station thermal systems
    Ma, Liangyu
    Duan, Wei
    Gao, Jianqiang
    Wang, Bingshu
    Tong, Zhensheng
    Dianli Xitong Zidonghue/Automation of Electric Power Systems, 2002, 26 (07): : 50 - 54