• Plataforma de vídeos del IIT
  • Twitter
  • LinkedIn
  • Asociación de Ingenieros del ICAI
  • Intranet
  • English
Ir arriba
Información del artículo en conferencia

Machine learning methods for detecting anomalies in a power transformer by monitoring its hot-spot temperature

C. Brighenti, M.A. Sanz-Bobi

4th International Conference on Power Engineering, Energy and Electrical Drives - IEEE POWERENG 2013, Estambul (Turquía). 13 mayo 2013


Resumen:
This paper analyzes and compares different machine learning methods such as decision trees, SOMs, MLPs and rough sets for the classification of the operation condition of a power transformer. The purpose is to construct a classification model able to estimate the hot-spot temperature as a function of other external input variables. The classifier would then be used to detect anomalous operation conditions of the transformer by comparing the observed and estimated hot-spot temperatures.


Palabras clave: Classification methods; anomaly detection; power transformer; decision trees; neural networks; rough sets


Fecha de publicación: mayo 2013.



Cita:
C. Brighenti, M.A. Sanz-Bobi, Machine learning methods for detecting anomalies in a power transformer by monitoring its hot-spot temperature, 4th International Conference on Power Engineering, Energy and Electrical Drives - IEEE POWERENG 2013, Estambul (Turquía). 13-17 Mayo 2013.


    Líneas de investigación:
  • *Inteligencia artificial aplicada al mantenimiento, diagnóstico y fiabilidad
  • *Predicción y Análisis de Datos

IIT-13-032A

pdf Solicitar el artículo completo a los autores