Power transformer fault type estimation using artificial neural network based on dissolved gas in oil analysis


Gunes I. , Gözütok A., Ucan O. N. , Kiremitci B.

ENGINEERING INTELLIGENT SYSTEMS FOR ELECTRICAL ENGINEERING AND COMMUNICATIONS, vol.17, no.4, pp.193-198, 2009 (Journal Indexed in SCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 17 Issue: 4
  • Publication Date: 2009
  • Journal Name: ENGINEERING INTELLIGENT SYSTEMS FOR ELECTRICAL ENGINEERING AND COMMUNICATIONS
  • Journal Indexes: Science Citation Index Expanded, Scopus
  • Page Numbers: pp.193-198

Abstract

In this paper, determine the fault type of failed power transformers with a few key gases with artficial neural network (ANN) using Levenberg-Marquardt algorithm is presented. Three Dissolved Gas in oil Analysis (DGA) criteria commonly used in industry was trained and tested with neural network Levenberg-Marquardt algorithm. Three key gases Methane (CH(4)), Ethylene (C(2)H(4)) and Acetylene (C(2)H(2)) were chosen for this study. Percentage of each gas used as inputs of ANN. The output is one of the fault types PD, D1, D2, T1, T2, T3. The results of this study are useful in development of a reliable transformer automated diagnostic system using artificial neural network. Multiple layer feedforward ANN is trained with Levenberg-Marquardt learning algorithm. This algorithm appears to be the fastest method for training moderate-sized feedforward neural networks. We determined best neural network topology and reached 100% diagnostic success.