ANFIS based model for power loss estimation of metal oxide surge arrester

This paper proposes a new method to estimate power loss characteristics of metal oxide surge arresters. The method was used to compute surge arrester power loss curve based on adaptive network based fuzzy inference system (ANFIS) and artificial neural network (ANN). Surge arrester operating history is an important factor that bears influence on its power loss. Degradation was, in this paper, introduced as a new index to represent operating history of metal oxide surge arrester. Therefore, applied voltage, temperature and degradation factor were considered as inputs in ANFIS system and ANN models to obtain accurate power loss which is a very important factor in surge arrester thermal stability. Degrading effect was undertaken by measurement voltage and current in varistors degraded by utilization in network. The results of the two artificial models were compared. Results show that ANFIS was more accurate than ANN. This study shows that the power loss characteristics of surge arrester are to a great extent accurately predictable using proposed artificial model.

[1]  M. G. Comber,et al.  Failure modes and energy absorption capability of ZnO varistors , 1999 .

[2]  S. Nishiwaki,et al.  Surge Discharge Capability and Thermal Stability of a Metal Oxide Surge Arrester , 1983, IEEE Transactions on Power Apparatus and Systems.

[3]  M. Kobayashi,et al.  Metal Oxide Surge Arrester , 1986, IEEE Transactions on Electrical Insulation.

[4]  Anil K. Jain,et al.  Artificial Neural Networks: A Tutorial , 1996, Computer.

[5]  S. Tominaga,et al.  Stability and Long Term Degradation of Metal Oxide Surge Arresters , 1980, IEEE Transactions on Power Apparatus and Systems.

[6]  G. St-Jean,et al.  Metal-oxide surge arrester operating limits defined by a temperature-margin concept , 1990 .

[7]  M. V. Lat,et al.  Thermal Properties of Metal Oxide Surge Arresters , 1983, IEEE Transactions on Power Apparatus and Systems.

[8]  O. Nigol.  Methods for analyzing the performance of gapless metal oxide surge arresters , 1992 .

[9]  S. Nishiwaki,et al.  Electro-thermal-mechanical computations in ZnO arrester elements , 2000 .

[10]  Bok-Hee Lee,et al.  Analysis of thermal and electrical properties of ZnO arrester block , 2010 .

[11]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[12]  Zhong Zheng,et al.  Computation of Arrester Thermal Stability , 2010, IEEE Transactions on Power Delivery.

[13]  M. V. Lat,et al.  Analytical Method for Performance Prediction of Metal Oxide Surge Arresters , 1985, IEEE Power Engineering Review.

[14]  H. Haron,et al.  Modeling of ANFIS in predicting TiN coatings roughness , 2013, 2013 5th International Conference on Computer Science and Information Technology.

[15]  S. Nishiwaki,et al.  Study of Thermal Runaway/Equivalent Prorated Model of A ZnO Surge Arrester , 1984, IEEE Power Engineering Review.

[16]  J. J. Woodworth,et al.  Watts loss of polymer housed surge arresters in a simulated Florida coastal climate , 1999 .

[17]  R. Zeng,et al.  Thermal Characteristics of High-Voltage Whole-Solid-Insulated Polymeric ZnO Surge Arrester , 2002, IEEE Power Engineering Review.

[18]  Hideyasu Ando,et al.  Development of high gradient zinc oxide nonlinear resistors and their application to surge arresters , 1998 .