Signal analysis and feature generation for pattern identification of partial discharges in high-voltage equipment

Perpiñan Lamigueiro, Oscar and Sánchez-Urán González, Miguel Ángel and Álvarez, Fernando and Ortego, Javier and Garnacho, Fernando (2013). Signal analysis and feature generation for pattern identification of partial discharges in high-voltage equipment. "Electric Power Systems Research", v. 95 ; pp. 56-65. ISSN 0378-7796. https://doi.org/10.1016/j.epsr.2012.08.016.

Description

Title: Signal analysis and feature generation for pattern identification of partial discharges in high-voltage equipment
Author/s:
  • Perpiñan Lamigueiro, Oscar
  • Sánchez-Urán González, Miguel Ángel
  • Álvarez, Fernando
  • Ortego, Javier
  • Garnacho, Fernando
Item Type: Article
Título de Revista/Publicación: Electric Power Systems Research
Date: February 2013
ISSN: 0378-7796
Volume: 95
Subjects:
Freetext Keywords: Partial discharge; Wavelet variance; Prony method; Feature generation; Clustering; Visualization tools
Faculty: E.U.I.T. Industrial (UPM)
Department: Ingeniería Eléctrica [hasta 2014]
Creative Commons Licenses: Recognition - Non commercial - Share

Available versions for this object

Warning

There is a more recent version of this electronic publication. Click here to see it.

Full text

[img]
Preview
PDF - Requires a PDF viewer, such as GSview, Xpdf or Adobe Acrobat Reader
Download (7MB) | Preview

Abstract

This paper proposes a method for the identification of different partial discharges (PDs) sources through the analysis of a collection of PD signals acquired with a PD measurement system. This method, robust and sensitive enough to cope with noisy data and external interferences, combines the characterization of each signal from the collection, with a clustering procedure, the CLARA algorithm. Several features are proposed for the characterization of the signals, being the wavelet variances, the frequency estimated with the Prony method, and the energy, the most relevant for the performance of the clustering procedure. The result of the unsupervised classification is a set of clusters each containing those signals which are more similar to each other than to those in other clusters. The analysis of the classification results permits both the identification of different PD sources and the discrimination between original PD signals, reflections, noise and external interferences. The methods and graphical tools detailed in this paper have been coded and published as a contributed package of the R environment under a GNU/GPL license.

More information

Item ID: 13521
DC Identifier: http://oa.upm.es/13521/
OAI Identifier: oai:oa.upm.es:13521
DOI: 10.1016/j.epsr.2012.08.016
Official URL: http://www.sciencedirect.com/science/article/pii/S0378779612002696
Deposited by: Oscar Perpiñán Lamigueiro
Deposited on: 04 Oct 2012 10:27
Last Modified: 01 Oct 2014 12:03
  • Logo InvestigaM (UPM)
  • Logo GEOUP4
  • Logo Open Access
  • Open Access
  • Logo Sherpa/Romeo
    Check whether the anglo-saxon journal in which you have published an article allows you to also publish it under open access.
  • Logo Dulcinea
    Check whether the spanish journal in which you have published an article allows you to also publish it under open access.
  • Logo de Recolecta
  • Logo del Observatorio I+D+i UPM
  • Logo de OpenCourseWare UPM