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Research on Temperature Rising Prediction of Distribution Transformer by Artificial Neural Networks

  • Wenxin Zhang
  • Jeng-Shyang Pan
  • Yen-Ming TsengEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 579)

Abstract

In order to predict the temperature rising of the distribution transformer by applying the artificial neural networks (ANNs) method analyze experimental data with the actual measured data and compared with the actual measured value to reach the relative errors investigation. The historical data of the working day are divided into three periods according to the varying loadings trend of load change emotion as the peak period, the general time period and the valley period. In experimental results, The average relative error of the peak period is 2.05%, the average relative error of the general period is 1.69%, the average relative error of the valley period is 1.25 %, and the working day average relative error is 1.60% for a day 24 hours. By Ann’s derivation the result has a very good prediction rate at temperature rising of distribution transformer.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Fujian Provincial Key Laboratory of Data Mining and Applications/School of Information Science and EngineeringFujian University of TechnologyFuzhouChina

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