Two Evolutionary Algorithms Based Parameter Identification of Excitation System

Excitation system plays a key role in realistic simulation and analysis of the dynamic performance of electrical power systems. However, simulation results with parameters provided by manufacture can usually not match the real operation. Therefore, parameter identification based on field data is focused on. In this paper, parameter identification methods based on particle swarm optimization (PSO) algorithm and genetic algorithm (GA) are applied. A standard model defined in the commercial software, BPA, is adopted in the study. By using the estimated parameters, the response of the standard model of BPA can match the filed data well. The identification results show the two methods are efficient. Moreover, comparing between the two methods shows that the optimization performance of PSO is better than that of GA.