Feature extraction method and neural network pattern recognition on time-resolved partial discharge signals

Nguyen Thi Ngoc Tho, Chandan Kumar Chakrabarty, Keem Siah Yap, Ahmad Basri Abd Ghani

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Magnetic sensor is a relatively new method to collect time-resolved partial discharge (PD) signals in XLPE cables. This paper proposes a simple yet effective method to recognize patterns of PD signals obtained from the magnetic sensor. The technique consists of wavelet transformation to de-noise the signals, statistical analysis to extract features and multi-layer perceptron back propagation (MLP BP) neural network to classify different types of PD signals. The result is elaborated in this paper.

Original languageEnglish
Title of host publication2011 IEEE Conference on Open Systems, ICOS 2011
Pages243-246
Number of pages4
DOIs
Publication statusPublished - 2011
Event2nd IEEE International Conference on Open Systems, ICOS 2011 - Langkawi, Malaysia
Duration: 25 Sep 201128 Sep 2011

Other

Other2nd IEEE International Conference on Open Systems, ICOS 2011
CountryMalaysia
CityLangkawi
Period25/09/1128/09/11

    Fingerprint

All Science Journal Classification (ASJC) codes

  • Hardware and Architecture
  • Software

Cite this