Parameters Estimation of Metal Oxide Surge Arrester Model via PSO-GWO Algorithm

— The appropriate modeling of surge arrester and its equivalent circuit parameters are significant issues. To ‎design a ‎suitable ‎lightning protection system, the surge arrester frequency-dependent model and its residual voltage should be ‎‎defined. In ‎this paper, particle swarm optimization with a ‎grey wolf optimization algorithm (PSO-GWO) has been implemented as an ‎optimization ‎‎algorithm to adjust the parameters of the surge arrester dynamic ‎model. According to the obtained results, the ‎best relative error values for the injected transient current have been obtained by the Pinceti model. For lightning impulse ‎current, the IEEE model has the best result and the lowest relative error values compared to the Fernandez and Pinceti ‎models. In addition, to compare the efficiency of the PSO-GWO, the obtained results for 10kA, 8/20µs have been ‎compared to the other optimization techniques results. The lowest error for the residual voltage amplitude of the surge ‎arrester model has been achieved by PSO-GWO algorithm. Besides, the modified PSO had the best results compared to the ‎genetic and the PSO techniques.‎