Wavelet-Based Power Network Disturbance Identifying Part I: Theory and Method

被引:0
|
作者
Chen, Gang [1 ]
Tang, Ming [1 ]
Ding, Lijie [1 ]
Zhang, Hua [1 ]
机构
[1] 24 Qinghua Rd, Chengdu 610072, Sichuan, Peoples R China
关键词
WAMS; wavelet transform; wavelet coefficient; disturbance identifying;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
It is important that control center operators be alerted to system disturbances, including where, when and what disturbance occurs, so that proper anticipatory actions can be promptly taken if necessary, avoiding oscillation spreads in the power network. In this paper, the wavelet multi-resolution analysis based method is proposed to identify power system disturbances. Energy of wavelet coefficients are used as a criterion to choose optimal wavelet function and decomposition scale, which are then used for obtaining the maximum wavelet coefficients by identifying the frequency signals from wide area measurement system (WAMS). The maximum wavelet coefficients are then selected to be the indicators for disturbance identifying. The detailed procedure and effectiveness of the proposed method is demonstrated by simulations of a 10-machine 39-bus system.
引用
收藏
页码:1313 / 1317
页数:5
相关论文
共 50 条
  • [1] Wavelet-Based Power Network Disturbance Identifying Part II: Application in Large Scale Power System
    Chen, Gang
    Zhou, Bo
    Zhang, Hua
    Ding, Lijie
    PROCEEDINGS OF THE 2015 4TH NATIONAL CONFERENCE ON ELECTRICAL, ELECTRONICS AND COMPUTER ENGINEERING ( NCEECE 2015), 2016, 47 : 1318 - 1321
  • [2] Wavelet-based neural network for power disturbance classification
    Gaing, ZL
    Huang, HS
    2003 IEEE POWER ENGINEERING SOCIETY GENERAL MEETING, VOLS 1-4, CONFERENCE PROCEEDINGS, 2003, : 1621 - 1628
  • [3] Power disturbance classifier using wavelet-based neural network
    Kim, Hongkyun
    Lee, Jinmok
    Choi, Jaeho
    Chung, Gyo-Bum
    2006 IEEE POWER ELECTRONICS SPECIALISTS CONFERENCE, VOLS 1-7, 2006, : 3279 - +
  • [4] Wavelet-based neural network for power disturbance recognition and classification
    Gaing, ZL
    IEEE TRANSACTIONS ON POWER DELIVERY, 2004, 19 (04) : 1560 - 1568
  • [5] Wavelet-based neural network approach to power quality disturbance recognition
    Kaewarsa, S.
    Attakitmongcol, K.
    IPEC: 2005 International Power Engineering Conference, Vols 1 and 2, 2005, : 266 - 271
  • [6] An effective wavelet-based feature extraction method for classification of power quality disturbance signals
    Uyar, Murat
    Yildirim, Selcuk
    Gencoglu, Muhsin Tunay
    ELECTRIC POWER SYSTEMS RESEARCH, 2008, 78 (10) : 1747 - 1755
  • [7] Surge disturbance detection using wavelet-based neural network
    Wang, Jing
    Shu, Hongchun
    Chen, Xueyun
    Dianli Xitong Zidonghue/Automation of Electric Power Systems, 2002, 26 (06): : 50 - 54
  • [8] Power quality disturbance waveform recognition using wavelet-based neural classifier - Part 2: Application
    Santoso, S
    Powers, EJ
    Grady, WM
    Parsons, AC
    IEEE TRANSACTIONS ON POWER DELIVERY, 2000, 15 (01) : 229 - 235
  • [9] Wavelet-based ARMA model Application in Power Network
    Wei, Wei
    Hao, Ma
    FRONTIERS OF MANUFACTURING AND DESIGN SCIENCE II, PTS 1-6, 2012, 121-126 : 1509 - +
  • [10] Power quality disturbance recognition using wavelet-based neural networks
    Kaewarsa, S.
    Attakitmongcol, K.
    TENCON 2005 - 2005 IEEE REGION 10 CONFERENCE, VOLS 1-5, 2006, : 1416 - 1420