Parameter Identification of Nonlinear Excitation System Based on Improved Adaptive Genetic Algorithm

Abstract

In order to obtain the parameters of excitation system, and improve the accuracy and efficiency of parameter identification much further, an improved adaptive genetic algorithm (IAGA) was exploited to the parameter identification of nonlinear generator excitation systems because of its strong global searching ability. An adaptive crossover and mutation operations was adopted, where the probability value can change along with the fitness, thus avoiding the premature convergence. Finally, the mathematical model of excitation system was built in Matlab/Simulink, and the test study shows that the proposed method can acquire accurate and reliable parameter values.