Mathematical modelling for coal fired supercritical power plants and model parameter identification using genetic algorithms

被引：4
|
作者：
Mohamed O. [1 ]
Wang J. [1 ]
Guo S. [1 ]
Wei J. [1 ]
Al-Duri B. [2 ]
Lv J. [3 ]
Gao Q. [3 ]
机构：
[1] School of Electrical, Electronics, and Computer Engineering, University of Birmingham, Edgbaston
[2] School of Chemical Engineering, University of Birmingham, Edgbaston
[3] Department of Thermal Engineering, Tsinghua University, Beijing
来源：
Lecture Notes in Electrical Engineering | 2011年 / 90 LNEE卷
基金：
英国工程与自然科学研究理事会;
关键词：
Coal-fired power generation; Genetic algorithms; Mathematical modeling; Supercritical boiler;
D O I：
10.1007/978-94-007-1192-1_1
中图分类号：
学科分类号：
摘要：
The paper presents the progress of our study of the whole process mathematical model for a supercritical coal-fired power plant. The modelling procedure is rooted from thermodynamic and engineering principles with reference to the previously published literatures. Model unknown parameters are identified using Genetic Algorithms (GAs) with 600MW supercritical power plant on-site measurement data. The identified parameters are verified with different sets of measured plant data. Although some assumptions are made in the modelling process to simplify the model structure at a certain level, the supercritical coal-fired power plant model reported in the paper can represent the main features of the real plant once-through unit operation and the simulation results show that the main variation trends of the process have good agreement with the measured dynamic responses from the power plants. © 2011 Springer Science+Business Media B.V.
引用
收藏
页码：1 / 13
页数：12
相关论文
共 45 条
  • [1] Modelling Study Of Supercritical Power Plant And Parameter Identification Using Genetic Algorithms
    Mohamed, Omar
    Wang, Jihong
    Guo, Shen
    Al-Duri, Bushra
    Wei, Jianlin
    WORLD CONGRESS ON ENGINEERING, WCE 2010, VOL II, 2010, : 973 - 978
  • [2] Solving the parameter identification problem of mathematical models using genetic algorithms
    Nyarko, EK
    Scitovski, R
    APPLIED MATHEMATICS AND COMPUTATION, 2004, 153 (03) : 651 - 658
  • [3] Parameter identification for LuGre friction model using genetic algorithms
    Liu, De-Peng
    PROCEEDINGS OF 2006 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-7, 2006, : 3419 - 3422
  • [4] Parameter identification of induction motor model using genetic algorithms
    Alonge, F
    D'Ippolito, F
    Ferrante, G
    Raimondi, FM
    IEE PROCEEDINGS-CONTROL THEORY AND APPLICATIONS, 1998, 145 (06): : 587 - 593
  • [5] A PARAMETER TUNING FOR DYNAMIC SIMULATION OF POWER-PLANTS USING GENETIC ALGORITHMS
    MIYAMOTO, Y
    MIYATAKE, T
    KUROSAKA, S
    MORI, Y
    ELECTRICAL ENGINEERING IN JAPAN, 1995, 115 (01) : 104 - 115
  • [6] Parameter Identification of Damage Models Using Genetic Algorithms
    P. A. Muñoz-Rojas
    E. L. Cardoso
    M. Vaz
    Experimental Mechanics, 2010, 50 : 627 - 634
  • [7] Parameter Identification of Damage Models Using Genetic Algorithms
    Munoz-Rojas, P. A.
    Cardoso, E. L.
    Vaz, M., Jr.
    EXPERIMENTAL MECHANICS, 2010, 50 (05) : 627 - 634
  • [8] Mathematical model for integrated coal fired thermal boiler using physical laws
    Sreepradha, Chandrasekharan
    Panda, Rames Chandra
    Bhuvaneswari, Natrajan Swaminathan
    ENERGY, 2017, 118 : 985 - 998
  • [9] Research on the parameter identification of LuGre tire model based on genetic algorithms
    Tan, Derong
    Wang, Yanyang
    Zhang, Li
    PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEMS AND KNOWLEDGE ENGINEERING (ISKE 2007), 2007,
  • [10] Parametric identification of integrated model of a coal-fired boiler in a thermal power plant
    Chandrasekharan, Sreepradha
    Panda, Rames C.
    Swaminathan, Bhuvaneswari Natrajan
    Panda, Atanu
    Thyagarajan, T.
    PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART A-JOURNAL OF POWER AND ENERGY, 2020, 234 (04) : 520 - 533
12345