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)


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.


  1. 1.
    Huang, Y.-C., Huang, C.-M.: Evolving wavelet networks for power transformer condition monitoring. IEEE Trans. Power Deliv. 17(2), 412–416 (2002)CrossRefGoogle Scholar
  2. 2.
    Wang, Z.Y., Liu, Y.L., Griffin, P.J.: A combined ANN and expert system tool for transformer fault diagnosis. IEEE Trans. Power Deliv. 13(4), 1224–1229 (1998)CrossRefGoogle Scholar
  3. 3.
    Contreras, J., Espinola, R., Nogales, F.J., Conejo, A.J.: ARIMA models to predict next-day electricity prices. IEEE Trans. Power Syst. 18(3), 1014–1020 (2003)CrossRefGoogle Scholar
  4. 4.
    Nogales, F.J., Contreras, J., Conejo, A.J., Espinola, R.: Forecasting next-day electricity prices by time series models. IEEE Trans. Power Syst. 17(2), 342–348 (2002)CrossRefGoogle Scholar
  5. 5.
    Ling, H., Leung, F.H.F., Lam, H.K., Lee, Y.S., Tam, P.K.S.: A novel genetic-algorithm-based neural network for short-term load forecasting. IEEE Trans. Ind. Electron. 50(4), 793–799 (2003)CrossRefGoogle Scholar
  6. 6.
    Barghinia, S., Ansarimehr, P., Habibi, H., Vafadar, N.: Short term load forecasting of Iran national power system using artificial neural network. In: Proceedings of the IEEE Power Tech, vol. 3 (2001)Google Scholar
  7. 7.
    Niu, D.X., Wang, H.Q., Gu, Z.H.: Short-term load forecasting using general regression neural network. IEEE Conf. Mach. Learn. Cybern. 7, 4076–4082 (2005)Google Scholar
  8. 8.
    Senjyu, T., Takara, H., Uezato, K., Funabashi, T.: One-hour-ahead load forecasting using neural network. IEEE Trans. Power Syst. 17, 113–118 (2002)Google Scholar
  9. 9.
    Kenuedy, S.P., Ivey, C.L.: Application, design and rating of transformers containing harmonic currents. In: Conference Record of 1990 Annual Pulp and Paper Industry Technical Conference IEEE (1990)Google Scholar
  10. 10.
    de Le’on, F., Semlyen, A.: A simple representation of dynamic hysteresis losses in power transformers. IEEE Trans. Power Deliv. 10(1) (1995)Google Scholar
  11. 11.
    Mori, H., Ogasawara, T.: A recurrent neural network for short-term load forecasting. In: Proceedings of the Second International Forum on Applications of Neural Networks to Power Systems, pp. 395–400 (1993)Google Scholar
  12. 12.
    Taylor, J.W., Buizza, R.: Neural network load forecasting with weather ensemble predictions? IEEE Trans. Power Syst. 17(3), 626–632 (2002)CrossRefGoogle Scholar
  13. 13.
    Iizaka, T., Matsui, T., Fukuyama, Y.: Novel daily peak load forecasting method using analyzable structured neural network. In: Transmission and Distribution Conference and Exhibition 2002, Asia Pacific IEEE/PES, vol 1, pp. 394–399 (2002)Google Scholar
  14. 14.
    Abu-EI-Magd, M.A., Findlay, R.D.: New Approach Using Artificial Neural Network and Time Series Models for Short Term Load Forecasting Neural Networks, IEEE CCECE 2003, Canadian Conference, vol. 3, pp. 1723–1726 (2003)Google Scholar
  15. 15.
    Wang, Z.Y., Liu, Y.L., Griffin, P.J.: A combined ANN and expert system tool for transformer fault diagnosis. IEEE Trans. Power Deliv. 13(4), 1224–1229 (1998)CrossRefGoogle Scholar

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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