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Electro-thermal model of metal oxide surge arrester based on adaptive method has been proposed in this paper. Finite element method has been used to model surge arrester. Power loss, which is major parameter in electro-thermal analysis, has been estimated as a heat source in proposed model using artificial neural network and adaptive network based fuzzy inference system. In addition to voltage and temperature, operating history is also an important factor that must be considered in power loss estimation process. In order to formulize surge arrester performance history, degradation factor has been suggested as a new index in this paper. Therefore, voltage, temperature and degradation factor have been used as inputs in artificial models. So as to train the artificial neural network and adaptive network based fuzzy inference system, experimental results have been obtained by laboratory tests on new and utilized surge arresters varistors. In this regards, high voltage experimental setup, chamber and an oven have been prepared to acquire modeling data. Also, the results of infrared thermal camera have been used to validate the results of proposed adaptive electro-thermal model. Moreover, convection and radiation as effective factors in surface to ambient heat transfer have been studied and compared. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]