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

被引：45
|
作者：
Aliprantis, DC
Sudhoff, SD
Kuhn, BT
机构：
[1] Purdue Univ, Dept Elect & Comp Engn, W Lafayette, IN 47907 USA
[2] Smart Spark Energy Syst Inc, Champaign, IL 61820 USA
来源：
IEEE TRANSACTIONS ON ENERGY CONVERSION | 2006年 / 21卷 / 01期
关键词：
brushless rotating machines; genetic algorithms (GAs); magnetic hysteresis; measurement; parameter estimation; synchronous generator excitation;
D O I：
10.1109/TEC.2005.847967
中图分类号：
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号：
0807 ; 0820 ;
摘要：
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.
引用
收藏
页码：148 / 154
页数：7
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