New phenemenon on power transformers and fault identification using artificial neural networks

Sengul M., Ozturk S., Cetinkaya H. B., Erfidan T.

ARTIFICIAL NEURAL NETWORKS - ICANN 2006, PT 2, vol.4132, pp.767-776, 2006 (SCI-Expanded, Scopus) identifier

  • Publication Type: Article / Article
  • Volume: 4132
  • Publication Date: 2006
  • Journal Name: ARTIFICIAL NEURAL NETWORKS - ICANN 2006, PT 2
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, EMBASE, MathSciNet, Philosopher's Index, zbMATH
  • Page Numbers: pp.767-776
  • Kocaeli University Affiliated: Yes

Abstract

In this paper voltage recovery after voltage dip that cause magnetizing inrush current which is a new phenomenon in power transformers are discussed and a new technique is proposed to distinquish internal fault conditions from no-fault conditions that is also containing these new phenomenons. The proposed differential algorithm is based on Artificial Neural Network (ANN). The training and testing data sets are obtained using SIMPOW-STRI power system simulation program and laboratory transformer. A novel neural network is designed and trained using back-propagation algorithm. It is seen that the proposed network is well trained and able to discriminate no-fault examples from fault examples with high accuracy.