NDLI: Modeling of combined cycle power plant based on a genetic algorithm parameter identification method

Content Provider	IEEE Xplore Digital Library
Author	Lin Gao Junrong Xia Yiping Dai
Copyright Year	2010
Description	Author affiliation: Institute of Turbomachinery, School of Energy & Power Engineering, Xi'an Jiaotong University, China, 710049 (Lin Gao; Junrong Xia; Yiping Dai)
Abstract	Classic combined cycle power plant models are often too complex for power system dynamic analysis, and hard to estimate the parameters accurately. The performances of parameter identification procedures have been significantly reduced by high-dimensional searches and strong nonlinear relationships. A new non-linear model is proposed to be suitable for the parameter identification procedure in this paper. An identification method based on an improved genetic algorithm (GA) is used for the modeling of a 400MW combined cycle power plant. The whole system is divided into six parts and an artificial disturbance is recorded for the identification of each part. The results show great consistence between identified model responses and the experimental data.
Starting Page	3369
Ending Page	3373
File Size	287462
Page Count	5
File Format	PDF
ISBN	9781424459582
e-ISBN	9781424459612
DOI	10.1109/ICNC.2010.5583666
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-08-10
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	gas turbine Parameter estimation combined cycle power plant Power system dynamics Data models paramenter identification dynamic modeling Fuels Mathematical model Turbines steam turbine
Content Type	Text
Resource Type	Article

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