A New Parameter Estimation Algorithm for Metal Oxide Surge Arrester

The modeling and exact determination of metal oxide surge arrester parameters are very important for arrester allocation, insulation coordination studies, and systems reliability calculations. In this article, a new method, which is a combination of particle swarm optimization and ant colony optimization methods, is proposed to obtain the parameters of metal oxide surge arrester models. The proposed method is named modified particle swarm optimization. The transient models of the metal oxide surge arrester have been simulated by using ATP-EMTP. The results of simulations have been applied to the program, which is based on the modified particle swarm optimization method, and can determine the fitness and parameters of different models. The validity and the accuracy of estimated parameters are assessed by comparing the predicted residual voltage with the experimental results.