A novel parameter estimation method for metal oxide surge arrester models

Accurate modelling and exact determination of Metal Oxide (MO) surge arrester parameters are very important for arrester allocation, insulation coordination studies and systems reliability calculations. In this paper, a new technique, which is the combination of Adaptive Particle Swarm Optimization (APSO) and Ant Colony Optimization (ACO) algorithms and linking the MATLAB and EMTP, is proposed to estimate the parameters of MO surge arrester models. The proposed algorithm is named Modified Adaptive Particle Swarm Optimization (MAPSO). 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 and the cognitive and the social parameters are self-adaptively adjusted. Also, to improve the global search capability and prevent the convergence to local minima, ACO algorithm is combined to the proposed APSO algorithm. The transient models of MO surge arrester have been simulated by using ATP-EMTP. The results of simulations have been applied to the program, which is based on MAPSO algorithm and can determine the fitness and parameters of different models. The validity and the accuracy of estimated parameters of surge arrester models are assessed by comparing the predicted residual voltage with experimental results.