Using Modified Fuzzy Particle Swarm Optimization Algorithm for Parameter Estimation of Surge Arresters Models

Accurate modeling and parameters identification of Metal Oxide Surge Arrester (MOSA) are very important for arrester allocation, systems reliability and insulation coordination studies. Several models with acceptable accuracy have been proposed to describe this behavior. It should be mentioned that the estimation of nonlinear elements of MOSAs is very important for all models. In this paper, a new method, which is the combination of Fuzzy Particle Swarm Optimization (FPSO) and Ant Colony Optimization (ACO) methods, is proposed to estimate the parameters of MOSA models. The proposed method is named Modified Fuzzy Particle Swarm Optimization (MFPSO). In the proposed algorithm, to overcome the drawback of the PSO algorithm (convergence to local optima), the inertia weight is tuned by using fuzzy rules. Also, to improve the global search capability and prevent the convergence to local minima, ACO algorithm is combined to proposed FPSO algorithm. The transient models of MOSA have been simulated by using ATP-EMTP. The results of simulations have been applied to the program, which is based on MFPSO method and can determine the fitness and parameters of different models. The validity and the accuracy of the estimated parameters are assessed by comparing the predicted residual voltage with the experimental results. Also, Using proposed algorithm, different surge arrester models and V-I characteristics determination methods have been compared.