Surge arrester leakage current modeling based on pollution layer electrical conductivity estimation

The surface leakage current (LC) of metal oxide surge arresters (MOSA) is highly dependent on environmental conditions. This factor influences the modeling of MOSA leakage current as an alternative solution to laboratory tests. In this paper, a new approach to modeling the surface leakage current of MOSA based on the estimation of the electrical conductivity (EC) of the contaminated layer is presented, which provides the ability to model the surge arrester LC in different environmental conditions with high accuracy. The estimation of the EC of the polluted layer has been performed using artificial intelligence (AI) based on laboratory tests considering the effect of uniform and non-uniform pollution, humidity, pollution intensity and voltage on three types of 20 kV silicon rubber surge arresters. Mean Squared Error (MSE) and Coefficient of Determination were used for assessing the ability of AI based method in EC estimation. Finite element method (FEM)-based software has been used for surge arrester modeling. The use of the estimated electrical conductivity characteristic in the FEM model has made it possible to evaluate the effect of the internal and external currents of the MOSA on the total leakage current in different scenarios. The comparison of the results obtained from proposed model and laboratory test indicate the capability of the proposed method in estimating the EC, modeling the LC, and their generalization to cases for which laboratory test results are not available.