Abstract

In the power system distribution, transformer has an important role to deliver electrical power to the consumer. The failure on the transformer could decrease the performance and remaining lifetime. Therefore, it is important to monitor the lifetime of the transformer to avoid the disturbance. This research proposes a simulation study to predict the lifetime of transformer using Nguyen-Widrow neural network. The parameters used for this research are current, temperature, and the lifetime of the transformer. Measurement is carried out on transformers with the rating of 20 kV/3S0-220 V and capacity of 100 kVA. The training and testing data of backpropagation neural network are PSD (power spectral density), and energy value resulted from the wavelet process. The expected output of the method is the prediction lifetime of the transformer. This study compares backpropagation methods. The results show that Nguyen-Widrow algorithm method can predict the lifetime of the transformer better than backpropagation neural network method.

Original languageEnglish
Title of host publication2018 International Conference on Computer Engineering, Network and Intelligent Multimedia, CENIM 2018 - Proceeding
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages305-310
Number of pages6
ISBN (Electronic)9781538675090
DOIs
Publication statusPublished - 2 Jul 2018
Event2018 International Conference on Computer Engineering, Network and Intelligent Multimedia, CENIM 2018 - Surabaya, Indonesia
Duration: 26 Nov 201827 Nov 2018

Publication series

Name2018 International Conference on Computer Engineering, Network and Intelligent Multimedia, CENIM 2018 - Proceeding

Conference

Conference2018 International Conference on Computer Engineering, Network and Intelligent Multimedia, CENIM 2018
Country/TerritoryIndonesia
CitySurabaya
Period26/11/1827/11/18

Keywords

  • PSD
  • distribution transformer
  • energy value
  • neural network
  • wavelet

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