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.‎