Optimum Parameter Identification Technique of Metal Oxide Surge Arrester Model Using Genetic Algorithm
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Metal oxide arresters have dynamic characteristics that are significant for overvoltage coordination studies involving fast front surges. This dynamic responses are frequency-dependent and can not be represented with the conventional non-linear V-I characteristics. Several models have been proposed to simulate this frequency-dependent behavior. But their accuracy are not acceptable in some cases. In this paper a new technique based on the genetic algorithm is proposed to obtain the best possible parameter values of the metal oxide arrester model. The existing GA method is developed by adding a weight term in the objective function which is related to the magnitude of input impulse current in time domain. The proposed method is applied on recommended surge arrester dynamic model from the IEEE W.G. 3.4.11 and results are obtained. The simulations were performed with the SIMULINK/MATLAB and the results were compared with the experimental data reported on the manufacturer’s catalogue available in the literature. The main innovation introduced by the paper lies in the implicitly of the criteria proposed for the parameter identification of the dynamic model of arresters. Effectiveness and simplicity of use make the proposed method a useful tool for insulation coordination studies involving steep front transients.
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