NDLI: Study of brushless excitation system parameters estimation based on improved genetic algorithm

Content Provider	IEEE Xplore Digital Library
Author	Shen Feng Xin Jianbo Wu Guoping Xie Yong-hong
Copyright Year	2008
Description	Author affiliation: Key Lab. of Power Syst. Protection & Dynamic Security Monitoring & Control, North China Electr. Power Univ., Beijing (Shen Feng; Xie Yong-hong)
Abstract	For estimating the parameters of brushless excitation system including nonlinear blocks, in this paper, a new optimization method based on improved genetic algorithm (IGA) is developed. A practicable test method which performs the linear block parameters and nonlinear parameters estimation respectively is presented. First, the model of brushless excitation system of AC1A developed by IEEE is presented. Then an improved GA based on real-coding is detailed. In this method a modified linear cross operator is used whose merits lies in both of child generation are feasible and one must be in the vicinity paternal generation so the convergence and precision of new method are improved. Finally, the single unit simulation system and AC1A excitation system are built in Matlab/Simulink. Utilizing the improved GA the linear block parameters and nonlinear block parameters of AC1A excitation system are estimated respectively using M serial pseudo random binary signal (PRBS) and reference voltage step signals. Compared with results received from simple GA the conclusions can be obtained that the improved GA is effective and the test method is practicable.
Starting Page	915
Ending Page	919
File Size	514677
Page Count	5
File Format	PDF
ISBN	9787900714138
DOI	10.1109/DRPT.2008.4523537
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-04-06
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	DRPT
Subject Keyword	Parameter estimation Genetic algorithms Power system modeling Power system reliability Power system simulation Testing Brushes System identification Signal processing Convergence Matlab/Simulink Brushless excitation system Genetic algorithm signals
Content Type	Text
Resource Type	Article

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