Arcing current Features Extraction Using Wavelet Transform

被引:0
|
作者
Chen, J. C. [1 ]
Phung, B. T. [1 ]
Zhang, D. [1 ]
Blackburn, T. R. [1 ]
Ambikairajah, E. [1 ]
机构
[1] Univ New S Wales, Sch Elect Engn & Telecommun, Sydney, NSW 2052, Australia
来源
PROCEEDINGS OF 2014 INTERNATIONAL SYMPOSIUM ON ELECTRICAL INSULATING MATERIALS (ISEIM 2014) | 2014年
关键词
high impedance fault; arcing; feature extraction;
D O I
暂无
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
High Impedance Fault (HIF) has long been a challenging problem in network protection. This is because of its difficulty to be detected and discriminated accurately and reliably. In real cases, arcing is usually associated with HIFs. In this paper, arcing currents produced by using different metals are analysed based on wavelet transform. The wavelet db4 is used and found to be sensitive in detecting high frequency transient. Test results show that the features for different current magnitudes are different. These features are studied to develop a simple detection criterion to distinguish arcing fault currents from the normal load.
引用
收藏
页码:221 / 224
页数:4
相关论文
共 50 条
  • [41] Image Fusion Using Quaternion Wavelet Transform and Multiple Features
    Chai, Pengfei
    Luo, Xiaoqing
    Zhang, Zhancheng
    IEEE ACCESS, 2017, 5 : 6724 - 6734
  • [42] Extracting effective features of SEMG using continuous wavelet transform
    Kilby, J.
    Hosseini, H. Gholam
    2006 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Vols 1-15, 2006, : 4382 - 4385
  • [43] Image Retrieval using Integrated Features of Binary Wavelet Transform
    Agarwal, Megha
    Maheshwari, R. P.
    2ND INTERNATIONAL CONFERENCE ON METHODS AND MODELS IN SCIENCE AND TECHNOLOGY (ICM2ST-11), 2011, 1414
  • [44] Classification of Arrhythmias Using Statistical Features in the Wavelet Transform Domain
    Lopez, Annet Deenu
    Joseph, Liza Annie
    PROCEEDINGS OF THE 2013 INTERNATIONAL CONFERENCE ON ADVANCED COMPUTING & COMMUNICATION SYSTEMS (ICACCS), 2013,
  • [45] Automated Detection of AMD using Wavelet Transform Based Features
    Sheela, N.
    Basavaraj, L.
    2016 INTERNATIONAL CONFERENCE ON ELECTRICAL, ELECTRONICS, COMMUNICATION, COMPUTER AND OPTIMIZATION TECHNIQUES (ICEECCOT), 2016, : 43 - 46
  • [46] Classification of Macula Edema Using Discrete Wavelet Transform Features
    Yun, Wong Li
    Koh, Joel E. W.
    JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS, 2014, 4 (04) : 628 - 633
  • [47] Facial Expression Recognition Using Stationary Wavelet Transform Features
    Qayyum, Huma
    Majid, Muhammad
    Anwar, Syed Muhammad
    Khan, Bilal
    MATHEMATICAL PROBLEMS IN ENGINEERING, 2017, 2017
  • [48] Olfaction Recognition by EEG Analysis Using Wavelet Transform Features
    Yavuz, Ebru
    Aydemir, Onder
    PROCEEDINGS OF THE 2016 INTERNATIONAL SYMPOSIUM ON INNOVATIONS IN INTELLIGENT SYSTEMS AND APPLICATIONS (INISTA), 2016,
  • [49] Detection of high impedance faults using current transformers for sensing and identification based on features extracted using wavelet transform
    Chen, Jichao
    Phung, Toan
    Blackburn, Trevor
    Ambikairajah, Eliathamby
    Zhang, Daming
    IET GENERATION TRANSMISSION & DISTRIBUTION, 2016, 10 (12) : 2990 - 2998
  • [50] Extraction feature of motor unbalance and interference of alternating current based on the Wavelet Transform
    Chen, Dongju
    Fan, Jinwei
    Zhang, Feihu
    RECENT TRENDS IN MATERIALS AND MECHANICAL ENGINEERING MATERIALS, MECHATRONICS AND AUTOMATION, PTS 1-3, 2011, 55-57 : 1028 - +