Review of Power-Quality Disturbance Recognition Using S-transform

被引:3
|
作者
Huang, Nantian [1 ,2 ]
Lin, Lin [1 ]
Huang, Wenhuan [3 ]
Qi, Jiajin [4 ]
机构
[1] Jilin Inst Chem Technol, Coll Informat & Control Engn, Changchun, Jilin, Peoples R China
[2] Harbin Inst Technol, Dept Elect Engn, Harbin, Peoples R China
[3] Jilin Inst Chem Technol, Coll Chem & Mat Engn, Jilin, Peoples R China
[4] State Grid Corp China, Hangzhou Elect Power Bureau, Hangzhou, Peoples R China
关键词
power quality(PQ); power quality(PQ) disturbance recognition; S- transform(ST); time-frequency resolution(TFR); feature extraction; NEURAL-NETWORK; CLASSIFICATION; EVENTS;
D O I
10.1109/CASE.2009.96
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Power quality (PQ) disturbance recognition is the foundation of power quality monitoring and analysis. The S-transform (ST) is an extension of the ideas of the continuous wavelet transform (CWT). It is based on a moving and scalable localizing Gaussian window. S-transform has better time frequency and localization property than traditional. With the excellent time frequency resolution (TFR) characteristics of the S-transform, ST is an attractive candidate for the analysis and feature extraction of power quality disturbances under noisy condition also has the ability to detect the disturbance correctly. This paper overviewed the theory of basis S-transform and two types of typical improved S-transform summarized their applications in the area of power quality disturbance recognition. The comparison between the ST-based method and other methods such as the wavelet-transform-based method for power-quality disturbance recognition shows the method has good scalability and very low sensitivity to noise levels. All of these show ST based methods has great potential for the future development of fully automated monitoring systems with online classification capabilities. The analysis direction and emphasis of studying about the power quality (PQ) disturbance recognition also put forward.
引用
收藏
页码:438 / +
页数:2
相关论文
共 50 条
  • [1] Power-quality disturbance recognition using S-transform
    Zhao, Fengzhan
    Yang, Rengang
    IEEE TRANSACTIONS ON POWER DELIVERY, 2007, 22 (02) : 944 - 950
  • [2] Power quality disturbance recognition using S-transform
    Zhao, Fengzhan
    Yang, Rengang
    2006 POWER ENGINEERING SOCIETY GENERAL MEETING, VOLS 1-9, 2006, : 809 - +
  • [3] Power system disturbance recognition using wavelet and S-transform techniques
    Reddy, Jaya Bharata
    Mohanta, Dusmanta Kumar
    Karan, B.M.
    International Journal of Emerging Electric Power Systems, 2004, 1 (02)
  • [4] Power Quality Disturbance Recognition Based on S-transform and SOM Neural Network
    Huang, Nantian
    Liu, Xiaosheng
    Xu, Dianguo
    Qi, Jiajin
    PROCEEDINGS OF THE 2009 2ND INTERNATIONAL CONGRESS ON IMAGE AND SIGNAL PROCESSING, VOLS 1-9, 2009, : 3742 - 3746
  • [5] Power Quality Disturbance Recognition Based on Multiresolution S-Transform and Decision Tree
    Zhong, Tie
    Zhang, Shuo
    Cai, Guowei
    Li, Yue
    Yang, Baojun
    Chen, Yun
    IEEE ACCESS, 2019, 7 : 88380 - 88392
  • [6] Power Quality Disturbance Classification using S-transform and Decision Tree
    Quan, Huimin
    Dai, Yuxing
    PACIIA: 2008 PACIFIC-ASIA WORKSHOP ON COMPUTATIONAL INTELLIGENCE AND INDUSTRIAL APPLICATION, VOLS 1-3, PROCEEDINGS, 2008, : 1563 - 1567
  • [7] Power Quality Disturbance Classification Using S-transform and Hidden Markov Model
    Hasheminejad, S.
    Esmaeili, S.
    Jazebi, S.
    ELECTRIC POWER COMPONENTS AND SYSTEMS, 2012, 40 (10) : 1160 - 1182
  • [8] POWER QUALITY DISTURBANCE CLASSIFICATION USING S-TRANSFORM AND RADIAL BASIS NETWORK
    Jayasree, T.
    Devaraj, D.
    Sukanesh, R.
    APPLIED ARTIFICIAL INTELLIGENCE, 2009, 23 (07) : 680 - 693
  • [9] Power Quality Disturbance Classification Using the S-Transform and Probabilistic Neural Network
    Wang, Huihui
    Wang, Ping
    Liu, Tao
    ENERGIES, 2017, 10 (01)
  • [10] Power quality disturbance detection based on S-transform and PNN
    Tang, Qiu
    Wang, Yaonan
    Guo, Siyu
    Teng, Zhaosheng
    Yi Qi Yi Biao Xue Bao/Chinese Journal of Scientific Instrument, 2009, 30 (08): : 1668 - 1673