Abstract

The aim of transformer monitoring is to observe the performance of the transformer in order to do predictive maintenance to prevent transformer's aging or damage. Damage or aging of isolation is a frequent problem in transformers. One cause of such insulation damage is the temperature rise in the transformer. Monitoring can also determine the remaining life of transformer through hot-spot temperature, which is obtained through top-oil and bottom-oil temperatures approximated by a particular function. Therefore, this research conducted a study on monitoring the temperature of transformer oil (top-oil) based on current, loading, and power factor for modeling using Backpropagation Neural Network (BPNN). For comparison, modeling also used Radial Basis Function Neural Network (RBFNN). The methods obtain prediction which results in transformer oil temperature by conducting training and testing of data verified by measuring top-oil temperature. The results of prediction from different capacities of transformers using both methods are then compared. Performance of the methods is shown by Mean Absolute Percentage Error (MAPE) value.

Original languageEnglish
Title of host publication2019 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation, ICAMIMIA 2019 - Proceeding
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages282-287
Number of pages6
ISBN (Electronic)9781728130903
DOIs
Publication statusPublished - Oct 2019
Event2019 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation, ICAMIMIA 2019 - Batu-Malang, Indonesia
Duration: 9 Oct 201910 Oct 2019

Publication series

Name2019 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation, ICAMIMIA 2019 - Proceeding

Conference

Conference2019 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation, ICAMIMIA 2019
Country/TerritoryIndonesia
CityBatu-Malang
Period9/10/1910/10/19

Keywords

  • Backpropagation Neural Network
  • Radial Basis Function
  • bottom-oil
  • current
  • distribution Transformer
  • power factor
  • temperature
  • top-oil

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