Property and ANN Simulating Model of Power Losses of ZnO Varistors

  • Han, Se-Won (Division of Electrical Materials, Korea Electrotechnology Research Institute) ;
  • He, Jin-Liang (Department of Electrical Engineering, Tsinghua University) ;
  • Cho, Han-Goo (Division of Electrical Materials, Korea Electrotechnology Research Institute)
  • Published : 1997.12.01

Abstract

ZnO varistors are widely used as surge arresters in power system based on their excellent nonlinearity. The property of power loss of ZnO varistors is related to the thermal stability and their life-spans of ZnO surge arresters. The power losses of ZnO varistors under different temperatures and applied voltages were measured, and the properties of power losses were analyzed. The Artificial Neural Network (ANN) was used to simulate the power losses properties of ZnO varistors which is an adaptive nonlinear dynamic system, and the results calculated by ANN simulating model were in good agreement with the tested ones.

Keywords

References

  1. IEEE Trans. on Power Apparatus and System v.PAS-104 no.10 Analytical method for performance prediction of metal oxide surge arresters M.V.Lat
  2. IEEE Trans. on Power Delivery v.PWRD-5 no.2 Metal-oxide surge arresters operating limits defined by temperature-margin concept Guy St Jean;Andre Petit
  3. IEEE Trans. on Power Delivery v.PWRD-1 no.1 A procedure for estimating the lifetime of gapless metal oxide surge arresters W.G.Carlson;T.K.Gupta;A.Sweetana
  4. IEEE Trans. on Power Apparatus and System v.PSA-102 no.2 Discharge capability and themal stability of a metal oxide surge arrester M.Kan;S.Nishiwaki;T.Sato;S.Kojima;S.Yanabu
  5. IEEE Trans. on power Apparatus and System v.PAS-100 no.5 Thermal stability and life of the gaplesssurge arrester M.Mizuna;M.Hayashi;T.Mitani
  6. IEEE Trans. on power Apparatus and System v.PAS-102 no.5 Evaluation of surge degradation of metal oxide surge arresters Y.Fujiwara;Y.Shibuga;M.Imataki;T.Nitta
  7. IEEE Trans. on Power Apparatus and System v.PAS-103 no.2 Study of runaway/equivalent prorated model of a ZnO surge arrester S.Nishiwaki;H.Kimura;T.Satoh;H.Mizoguchi;S.Yanabu
  8. Adaptive pattern recognition and neural networks Yoh Han Pao