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

The appropriate modeling of surge arrester and itsequivalent circuit parameters are significant issues. To designa suitable lightning protection system, the surge arresterfrequency-dependent model and its residual voltage shouldbe defined. In this paper, particle swarm optimization with a greywolf optimization algorithm (PSO-GWO) has been implementedas an optimization algorithm to adjust the parameters of thesurge arrester dynamic model. According to the obtained results,the best relative error values for the injected transient currenthave been obtained by the Pinceti model. For lightningimpulse current, the IEEE model has the best result and thelowest relative error values compared to the Fernandez andPinceti models. In addition, to compare the efficiency of the PSOGWO,the obtained results for ۱۰kA, ۸/۲۰μs have been comparedto the other optimization techniques results. The lowest error forthe residual voltage amplitude of the surge arrester model hasbeen achieved by PSO-GWO algorithm. Besides, the modifiedPSO had the best results compared to the genetic and the PSOtechniques.

کلیدواژه ها:

Surge Arrester Dynamic model ، Residual Voltage ، Optimization Algorithm