Title :
The dynamic neural network model of a ultra super-critical steam boiler unit
Author :
Xiangjie Liu ; Xuewei Tu ; Guolian Hou ; Jihong Wang
Author_Institution :
Dept. of Autom., North China Electr. Power Univ., Beijing, China
fDate :
June 29 2011-July 1 2011
Abstract :
Thermal power unit is an energy conversion system consisting of the boiler, the turbine and their auxiliary machines respectively. It is a complicated multivariable system with strong nonlinearity, uncertainty and multivariable coupling. These characters will be more evident with the unit tending to large-capacity and high-parameter. It is expensive to build the model of the unit using conventional method. The paper presents modeling of a 1000MW ultra supercritical once-through boiler unit. Based on these field data, two different neural networks are used to model the thermal power unit. The simulation results validate the efficiency of the neural networks in modelling the ultra supercritical unit.
Keywords :
boilers; neural nets; power engineering computing; dynamic neural network model; energy conversion system; multivariable system; power 1000 MW; thermal power unit; ultra supercritical once-through steam boiler unit; Boilers; Data models; Fuels; Fuzzy neural networks; Neural networks; Nonlinear dynamical systems; Power generation;
Conference_Titel :
American Control Conference (ACC), 2011
Conference_Location :
San Francisco, CA
Print_ISBN :
978-1-4577-0080-4
DOI :
10.1109/ACC.2011.5991224
