A transformer differential protection based on finite impulse response artificial neural network

被引：6
|
作者：
Orille, AL [1 ]
Khalil, N [1 ]
Valencia, JA [1 ]
机构：
[1] Univ Antioquia, Dept Elect Engn, Medellin, Colombia
来源：
COMPUTERS & INDUSTRIAL ENGINEERING | 1999年 / 37卷 / 1-2期
关键词：
protection; differential; transformer; digital; neural; network;
D O I：
10.1016/S0360-8352(99)00103-5
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
This paper presents the application of a finite impulse response artificial neural network (FIRANN) on digital differential protection design for a three-phase transformer. The neural network inputs are normalized sampled current data Any pre-processing signal as in other neural network applications is not needed. The network was trained to identify external fault on load side besides internal fault as in the other differential protection. The FIRANN has 6 inputs and 2 outputs. The first output goes on when there is an internal fault while the second output goes on in case of external fault. The simulated system used to get data for training and testing the neural network is presented. The neural network architecture and some of the obtained results are reported. (C) 1999 Elsevier Science Ltd. All rights reserved.
引用
收藏
页码：399 / 402
页数：4
相关论文
共 50 条
  • [1] Transformer differential protection based on finite impulse response artificial neural network
    Orille, A.L.
    Khalil, Nabil
    Valencia V, J.A.
    [J]. Computers and Industrial Engineering, 1999, 37 (01): : 399 - 402
  • [2] DIFFERENTIAL PROTECTION OF TRANSFORMER BASED ON ARTIFICIAL NEURAL NETWORK AND PROGRAMMABLE LOGIC
    Almeida, Isaque S.
    Santos, Ricardo C.
    [J]. INTERNATIONAL JOURNAL OF POWER AND ENERGY SYSTEMS, 2015, 35 (04): : 154 - 160
  • [3] Power transformer differential protection scheme based on symmetrical component and artificial neural network
    Khorashadi-Zadeh, H
    [J]. NEUREL 2004: SEVENTH SEMINAR ON NEURAL NETWORK APPLICATIONS IN ELECTRICAL ENGINEERING, PROCEEDINGS, 2004, : 261 - 265
  • [4] Differential Protection of Three Phase Transformer Using Artificial Neural Network
    Thote, P. B.
    Daigavane, M. B.
    [J]. 2015 7TH INTERNATIONAL CONFERENCE ON EMERGING TRENDS IN ENGINEERING & TECHNOLOGY (ICETET), 2015, : 76 - 81
  • [5] Transformer protection principle based on the artificial neural network model
    Li, Gong-Hua
    [J]. Dianli Xitong Baohu yu Kongzhi/Power System Protection and Control, 2010, 38 (10): : 26 - 30
  • [6] A New Transformer Protection Based on the Artificial Neural Network Model
    Chen, MingJie
    Zeng, Xin
    Li, GongHua
    Luo, Jian
    [J]. 2008 WORLD AUTOMATION CONGRESS PROCEEDINGS, VOLS 1-3, 2008, : 1539 - +
  • [7] Power transformer differential protection scheme based on wavelet transform and artificial neural network algorithms
    Khorashadi-Zadeh, H
    Sanaye-Pasand, M
    [J]. UPEC 2004: 39TH INTERNATIONAL UNIVERSITITIES POWER ENGINEERING CONFERENCE, VOLS 1-3, CONFERENCE PROCEEDINGS, 2005, : 747 - 752
  • [8] Transformer differential protection with neural network based inrush stabilization
    Rebizant, Waldemar
    Bejmert, Daniel
    Schiel, Ludwig
    [J]. 2007 IEEE LAUSANNE POWERTECH, VOLS 1-5, 2007, : 1209 - +
  • [9] A Novel Methodology for Power Transformer Differential Protection by incorporating Artificial Neural Network
    Bhatt, Nimish
    Rahi, O. P.
    Bharadwaj, Nitish
    [J]. 2016 INTERNATIONAL CONFERENCE ON ELECTRICAL POWER AND ENERGY SYSTEMS (ICEPES), 2016, : 68 - 74
  • [10] New artificial neural network based magnetizing inrush detection in digital differential protection for large transformer
    Lu, YP
    Lai, LL
    Hua, LD
    [J]. Proceedings of 2005 International Conference on Machine Learning and Cybernetics, Vols 1-9, 2005, : 441 - 447
12345