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

C. Brighenti, M.A. Sanz-Bobi

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.


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

4th International Conference on Power Engineering, Energy and Electrical Drives - IEEE POWERENG 2013. Estambul, Turquia. 13-17 Mayo 2013

Published: May 2013.


    Research topics:
  • *Artificial intelligence applied to maintenance diagnosis and reliability
  • *Forecasting and data mining

IIT-13-032A

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