Application of evolutionary programming to transient and subtransient parameter estimation

被引:9
作者
Lai, LL
Ma, JT
机构
[1] Energy Systems Group, Dept. of Electrical, Electronic and Information Engineering, City University
关键词
electrical machines; identification and modelling; artificial intelligence; evolutionary programming;
D O I
10.1109/60.537003
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
This paper presents an artificial intelligence approach of using evolutionary programming to estimate the transient and subtransient parameters of a generator under normal operation, The estimation using evolutionary programming is compared with that using corrected extended Kalman filter. The comparisons with both simulation and micromachine test results show that evolutionary programming is robust to search the real values of parameters even when the data are highly contaminated by noises, while with extended Kalman filter, the estimation tends to diverge with such data.
引用
收藏
页码:523 / 529
页数:7
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