A new look at classification of transformer normal and abnormal currents

Mohamed Abdel-Hafez, Ahmed M. Gaouda

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

Abstract

The paper proposes an enhanced wavelet-based feature extraction technique to classify transformer inrush currents (TIC) and transformer internal faults (TIF). The proposed tool utilizes the number of wavelet coefficients of local maxima as current signal slides into Kaiser's window. The general pattern of number of coefficients of local maxima at the first three resolutions are used to design a new automated tool for monitoring and classifying abnormal conditions in power transformers. The proposed monitoring technique is evaluated using large data sets.

Original languageEnglish
Title of host publicationMELECON 2010 - The 15th IEEE Mediterranean Electrotechnical Conference
Subtitle of host publicationBook of Abstracts
Pages830-834
Number of pages5
DOIs
Publication statusPublished - 2010
Event15th IEEE Mediterranean Electrotechnical Conference, MELECON 2010 - Valletta, Malta
Duration: Apr 25 2010Apr 28 2010

Publication series

NameProceedings of the Mediterranean Electrotechnical Conference - MELECON

Other

Other15th IEEE Mediterranean Electrotechnical Conference, MELECON 2010
Country/TerritoryMalta
CityValletta
Period4/25/104/28/10

Keywords

  • Inrush current
  • Kaiser's window
  • Multi-resolution analysis
  • Transformer faults
  • Wavelet transform

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

Fingerprint

Dive into the research topics of 'A new look at classification of transformer normal and abnormal currents'. Together they form a unique fingerprint.

Cite this