Advance in Neural Networks for Power Transformer Condition Assessment

  • Kun-Yuan Huang
  • Yann-Chang Huang
  • Hsing-Feng Chen
  • Hsieh-Ping Chen
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 293)

Abstract

Artificial neural networks (ANN) have emerged as rapidly evolving and highly practical approaches for condition assessment of power transformers. This study reviewed different ANN approaches for assessing power transformer conditions by discussing historical developments and presenting state-of-the-art ANN methods. Relevant publications from international journals covering a broad range of ANN methods were reviewed. This paper concludes that no single ANN approach enables detection of all faults of power transformers; therefore, overall and reliable assessment of power transformer conditions is necessary. Moreover, the most effective condition assessment technique is to combine artificial intelligent approaches to form hybrid intelligence-based systems and to aggregate them into an overall evaluation. This paper is helpful in the academics, research and engineering community, which is working on condition assessment of transformer fault diagnosis using artificial intelligence.

Keywords

Neural networks Power transformers Condition assessment 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Kun-Yuan Huang
    • 1
  • Yann-Chang Huang
    • 1
  • Hsing-Feng Chen
    • 1
  • Hsieh-Ping Chen
    • 1
  1. 1.Department of Electrical Engineering, ICITESCheng Shiu UniversityKaohsiungTaiwan, Republic of China

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