Identification for stability analysis of three-phase bridge rectifier feeding the parallel controlled buck converters by artificial intelligence techniques
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Abstract
Artificial intelligent techniques are very popular in engineering works. This paper presents three techniques as follows: genetic algorithm, particle swarm optimization and adaptive tabu search to identify the accurate parameters in the mathematical model of AC-DC power system feeding the parallel controlled buck converters. The controlled power converters behave as the constant power loads in which they can significantly degrade the system stability. Hence, the stability analysis is necessary to predict the unstable point of the system. However, it is not only the behaviors of the controlled power converters but the parameters of the system also affect the system stability. Some parameters cannot be measured by the instrument directly. Therefore, the artificial intelligent techniques will be used to search the parameter values that correctly represent the system response. After these parameters are achieved, the dynamic model with accurate parameters will be used for the stability analysis by the eigenvalue theorem via the mathematical model. The stability analysis verification will be confirmed by the simulation and experimental results.
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References
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