Aplicación de redes neuronales probabilísticas en la detección de fallas incipientes en transformadores


Authors

  • Juan Andres Cadena
  • Juan Mauricio Cadena
  • Sandra Milena Pérez Londoño

DOI:

https://doi.org/10.22517/23447214.3153

Abstract

En este documento se presenta una herramienta de diagnóstico basada en metodologías de inteligencia artificial como las redes neuronales probabilísticas, utilizadas en la detección de fallas en transformadores. Utilizando los resultados entregados por ensayos realizados sobre el aceite de un transformador, a través del análisis de gases disueltos (AGD), se obtienen los conjuntos de datos para el entrenamiento y posterior prueba para la red neuronal. Los resultados obtenidos se comparan con los calculados mediante metodologías dadas en el estándar IEEE C57-104, permitiendo obtener con ello un criterio de validez sobre la metodología artificial implementada.

Downloads

Download data is not yet available.

Downloads

Published

2008-08-30

How to Cite

Cadena, J. A., Cadena, J. M., & Pérez Londoño, S. M. (2008). Aplicación de redes neuronales probabilísticas en la detección de fallas incipientes en transformadores. Scientia Et Technica, 2(39). https://doi.org/10.22517/23447214.3153

Issue

Vol. 2 No. 39 (2008)

Section

Eléctrica

License

The undersigned authors declare that the article submitted to the journal Scientia et Technica is an original work and that all its content is free of third-party copyright restrictions or has the corresponding authorizations. Consequently, the authors assume responsibility for any litigation or claim related to intellectual property rights, releasing the Technological University of Pereira and the journal Scientia et Technica from any liability.

If the submitted work is accepted for publication, the authors retain copyright to the article and grant the journal Scientia et Technica the right of first publication, as well as a non-exclusive, perpetual license to reproduce, edit, distribute, display, and publicly communicate the article in any medium or format, including print, electronic, databases, repositories, the Internet, or other scientific dissemination systems. The authors agree that the article will be published in open access and distributed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0).

The journal Scientia will respect in all cases the moral rights of the authors, in accordance with the provisions of article 30 of Law 23 of 1982 of the Republic of Colombia, recognizing the authorship of the work, the right to integrity and the right of disclosure, which are inalienable and non-waivable.