Estimation of the Electric Field across Medium Voltage Surge Arresters Using Artificial Neural Networks

  • Lambros Ekonomou
  • Christos A. Christodoulou
  • Valeri Mladenov
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 459)


Artificial neural networks (ANNs) are addressed in order to estimate the electric field across medium voltage surge arresters, information which is very useful for diagnostic tests and design procedures. Actual input and output data collected from hundreds of measurements carried out in the High Voltage Laboratory of the National Technical University of Athens (NTUA) are used in the training, validation and testing process. The developed ANN method can be used by laboratories and manufacturing/retail companies dealing with medium voltage surge arresters which either face a lack of suitable measuring equipment or want to compare/verify their own measurements.


Artificial neural networks electric field measurements surge arresters 


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  1. 1.
    Aggarwal, R., Song, Y.: Artificial neural networks in power systems. III Examples of applications in power systems. Power Engin. Journal 12(6), 279–287 (1998)CrossRefGoogle Scholar
  2. 2.
    Mahanty, R.N., Gupta, P.B.D.: Application of RBF neural network to fault classification and location in transmission lines. IEE Proc-Gen Tran. Distr. 151(2), 201–212 (2004)Google Scholar
  3. 3.
    Mazon, A.J., Zamora, I., Gracia, J., Sagastabeutia, K.J., Saenz, J.R.: Selecting ANN structures to find transmission faults. IEEE Computer Appl. in Power 14(3), 44–48 (2001)CrossRefGoogle Scholar
  4. 4.
    Vasilic, S., Kezunovic, M.: An improved neural network algorithm for classifying the transmission line faults. Power Engin. Society Winter Meeting 2, 918–923 (2001)CrossRefGoogle Scholar
  5. 5.
    Gardoso, G., Rolim, J.G., Zurn, H.H.: Application of neural-network modules to electric power system fault section estimation. IEEE Trans. on PWRD 19(3), 1034–1041 (2004)Google Scholar
  6. 6.
    Schmidt, H.P.: Application of artificial neural networks to the dynamic analysis of the voltage stability problem. IEE Proc-Gen Tran. Distr. 144(6), 371–376 (1997)Google Scholar
  7. 7.
    Paucar, V.L., Rider, M.J.: Artificial neural networks for solving the power flow problem in electric power systems. Electric Power Systems Research 62, 139–144 (2002)CrossRefGoogle Scholar
  8. 8.
    Dash, P.K., Pradhan, A.K., Panda, G.: Application of minimal radial basis function neural network to distance protection. IEEE Trans. on PWRD. 16(1), 68–74 (2001)Google Scholar
  9. 9.
    Coury, D.V., Jorge, D.C.: Artificial neural network approach to distance protection of transmission lines. IEEE Trans. on PWRD 13(1), 102–108 (1998)Google Scholar
  10. 10.
    Cline, P., Lannes, W., Richards, G.: Use of pollution monitors with a neural network to predict insulator flashover. Electric Power Systems Research 42, 27–33 (1997)CrossRefGoogle Scholar
  11. 11.
    Ahmad, A.S., Ghosh, P.S., Aljunid, S.A.K., Said, H.A.I., Hussain, H.: Artificial neural network for contamination severity assessment of high voltage insulators under various meteorological conditions. In: AUPEC, Perth (2001)Google Scholar
  12. 12.
    Miti, G.K., Moses, A.J.: Neural network-based software tool for predicting magnetic performance of strip-wound magnetic cores at medium to high frequency. IEE Proc-Sci. Meas. Technol. 151(3), 181–187 (2004)Google Scholar
  13. 13.
    Martinez, J.A., Gonzalez-Molina, F.: Statistical evaluation of lightning overvoltages on overhead distribution lines using neural networks. Power Engin. Society Winter Meeting 3, 1133–1138 (2001)Google Scholar
  14. 14.
    Sidhu, T.S., Singh, H., Sachdev, M.S.: Design, implementation and testing of an artificial neural network based fault direction discrimination for protecting transmission lines. IEEE Trans. on PWRD 10(2), 697–706 (1995)Google Scholar
  15. 15.
    Hinrichsen, V.: Metal-oxide surge arresters, 1st edn. Siemens (2001)Google Scholar
  16. 16.
    James, R.E., Su, Q.: Condition assessment of high voltage insulation in power system equipment, 1st edn. IET Power and Energy Series, p. 53 (2008)Google Scholar
  17. 17.
    Vahidi, B., Nasab, R.S., Moghani, J., Sh., K.S.A., Hosseinian, S.H.: Three dimensional analyses of electric field and voltage distribution on ZnO surge arrester with broken sheds. In: 2005 IEEE/PES Trans. and Distrib. Conf. & Exhib.: Asia and Pacific, Dalian, China (2005)Google Scholar
  18. 18.
    Meshkatoddini, M.R.: Study of the electric field intensity in bushing integrated ZnO surge arresters by means of finite element analysis. In: COSMOL Users Conf., Boston (2006)Google Scholar
  19. 19.
    Lundquist, J., Stenstrom, L., Schei, A., Hansen, B.: New method of the resistive leakage currents of metal-oxide surge arresters in service. IEEE Trans. on PWRD 5(4), 1811–1822 (1990)Google Scholar
  20. 20.
    Vahidi, B., Nasab, R.S., Moghani, J.S.: Analysis of electric field and voltage distributions on ZnO surge arrester for polluted condition. In: XIV Int. Symp. on High Voltage Engin., Tsinghua University, Beijing, China (2005)Google Scholar
  21. 21.
    Karthik, R.: A novel analysis of voltage distribution in zinc oxide arrester using finite element method. Int. J. of Recent Trends in Engineering 1(4), 1–3 (2009)Google Scholar
  22. 22.
    Han, S.J., Zou, J., Gu, S.Q., He, J.L., Yuan, J.S.: Calculation of the potential distribution of high voltage metal oxide arrester by using an improved semi-analytic finite element method. IEEE Trans. on Magnetics 41(5), 1392–1395 (2005)CrossRefGoogle Scholar
  23. 23.
    Abe, S.: Neural networks and fuzzy systems. Kluwer Academic Publishers, Boston (1997)CrossRefzbMATHGoogle Scholar
  24. 24.
    Haykin, S.: Neural Networks: a comprehensive foundation. MacMillan College Publishing Company, New York (1994)zbMATHGoogle Scholar
  25. 25.
    Maghami, P.G., Sparks, D.W.: Design of neural networks for fast convergence and accuracy: dynamics and control. IEEE Trans. on Neural Networks 11(1), 113–123 (2000)CrossRefGoogle Scholar
  26. 26.
    Nolles, O.: Nonlinear system identification: from classical approaches to neural networks and fuzzy models. Springer, Berlin (2001)CrossRefGoogle Scholar
  27. 27.
    Lippmann, R.: An introduction to computing with neural nets. IEEE ASSP Magazine 4(2), 4–22 (1987)CrossRefGoogle Scholar
  28. 28.
    Tamura, S.I., Tateishi, M.: Capabilities of a four-layered feedforward neural network: four layers versus three. IEEE Trans. on Neural Nets 8(2), 251–255 (1997)CrossRefGoogle Scholar
  29. 29.
    Demuth, H., Beale, M.: Neural network toolbox user’s guide for use with MATLAB (2002)Google Scholar
  30. 30.
    Hagan, M.T., Demuth, H.P., Beale, M.: Neural network design. PWS Publishing, Boston (1996)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Lambros Ekonomou
    • 1
  • Christos A. Christodoulou
    • 2
  • Valeri Mladenov
    • 3
  1. 1.School of Engineering and Mathematical Sciences, Department of Electrical and Electronic EngineeringCity University LondonLondonUnited Kingdom
  2. 2.School of Electrical and Computer Engineering, High Voltage LaboratoryNational Technical University of AthensAthensGreece
  3. 3.Department of Theoretical Electrical EngineeringTechnical University of SofiaBulgaria

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