Modeling of a 1000 MW power plant ultra super-critical boiler system using fuzzy-neural network methods

被引：115
作者：
Liu, X. J. [1 ]
Kong, X. B. [1 ]
Hou, G. L. [1 ]
Wang, J. H. [2 ]
机构：
[1] N China Elect Power Univ, State Key Lab Alternate Elect Power Syst Renewabl, Beijing 102205, Peoples R China
[2] Univ Warwick, Sch Engn, Coventry CV4 7AL, W Midlands, England
来源：
ENERGY CONVERSION AND MANAGEMENT | 2013年 / 65卷
基金：
中国国家自然科学基金; 英国工程与自然科学研究理事会;
关键词：
Fuzzy neural network; Modeling; Ultra super-critical boiler; PREDICTIVE CONTROL; TEMPERATURE; SIMULATION;
D O I：
10.1016/j.enconman.2012.07.028
中图分类号：
O414.1 [热力学];
学科分类号：
摘要：
A thermal power plant is an energy conversion system consisting of boilers, turbines, generators and their auxiliary machines respectively. It is a complex multivariable system associated with severe nonlinearity, uncertainties and multivariable couplings. These characters will be more evident when the system is working at a higher level energy conversion capacity. In many cases, it is almost impossible to build a mathematical model of the system using conventional analytic methods. The paper presents our recent work in modeling of a 1000 MW ultra supercritical once-through boiler unit of a power plant. Using on-site measurement data, two different structures of neural networks are employed to model the thermal power plant unit. The method is compared with the typical recursive least squares (RLSs) method, which obviously demonstrated the merit of efficiency of the neural networks in modeling of the 1000 MW ultra supercritical unit. (C) 2012 Elsevier Ltd. All rights reserved.
引用
收藏
页码：518 / 527
页数：10
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