Genetic algorithm-based parameter identification of a hysteretic brushless exciter model

In this paper, a parameter identification procedure for a recently proposed hysteretic brushless exciter model is discussed. The model features average-value representation of all rectification modes, and incorporation of magnetic hysteresis in the d-axis main flux path using Preisach's theory. Herein, a method for obtaining the model's parameters from the waveforms of exciter field current and main alternator terminal voltage is set forth. In particular, a genetic algorithm is employed to solve the optimization problem of minimizing the model's prediction error during a change in reference voltage level.