Abstract

The distribution transformer is one of the vital components in the power system distribution, which deliver electricity power to the consumer. Various disturbances on the transformers can cause a decrease of their performance, so that they cannot reach the operation life. This study proposes a simulation study to predict the transformer oil age by using wavelet transform and backpropagation neural network. Transformer's current measurement was carried out in North Surabaya with a rating of 20 KV/380-220V and capacity of $100~\mathrm {k}\mathrm {V}\mathrm {A}$. The secondary current of the distribution transformer has been processed using the haar wavelet to obtain the detail coefficients, which is used to calculate the energy and PSD (power spectral density) value. Energy value and PSD are the input data on training and testing of back propagation neural network, while the output (target) is the transformer oil age. The simulation results show that the proposed method can predict the transformer oil age with an accuracy rate of 89.5795%.

Original languageEnglish
Title of host publicationProceedings of 2018 10th International Conference on Information Technology and Electrical Engineering
Subtitle of host publicationSmart Technology for Better Society, ICITEE 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages202-207
Number of pages6
ISBN (Electronic)9781538647394
DOIs
Publication statusPublished - 13 Nov 2018
Event10th International Conference on Information Technology and Electrical Engineering, ICITEE 2018 - Bali, Indonesia
Duration: 24 Jul 201826 Jul 2018

Publication series

NameProceedings of 2018 10th International Conference on Information Technology and Electrical Engineering: Smart Technology for Better Society, ICITEE 2018

Conference

Conference10th International Conference on Information Technology and Electrical Engineering, ICITEE 2018
Country/TerritoryIndonesia
CityBali
Period24/07/1826/07/18

Keywords

  • Backpropagation neural network
  • Distribution transformer
  • Energy value
  • Haar wavelet
  • PSD

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