Estimation of parameters of metal-oxide surge arrester models using Big Bang-Big Crunch and Hybrid Big Bang-Big Crunch algorithms

Abstract

Metal oxide surge arrester accurate modeling and its parameter identification are very important for insulation coordination studies, arrester allocation, and system reliability since quality and reliability of lightning performance studies can be improved with the more efficient representation of the arresters´ dynamic behavior. In this work, the Big Bang -Big Crunch (BB-BC) and Hybrid Big Bang -Big Crunch (HBB-BC) optimization algorithms were used to select the optimum surge arrester model equivalent circuit parameter values, minimizing the error between the simulated peak residual voltage value and that given by the manufacturer. The proposed algorithms were applied to 63 kV and 230 kV metal oxide surge arresters. The results obtained showed that by using this method, the maximum percentage error was below 1.5%.