Title :
Modeling, identification and control of a heavy duty industrial gas turbine
Author :
Yousefi, I. ; Yari, M. ; Shoorehdeli, Mahdi Aliyari
Author_Institution :
R&D Dept., Manuf. Co. - MECO, Karaj, Iran
Abstract :
In this paper, modeling, identification and control of a real 162MW heavy duty industrial gas turbine is taken into account. This work is aimed to introduce a simple and comprehensive model to test various controllers. Rowen´s model is used to present the mechanical behavior of the gas turbine, while the identification of it is done using a feedforward neural network. The control rules of the turbine are applied on both models and a comparison of the results is also presented.
Keywords :
gas turbines; identification; modelling; neurocontrollers; 162MW heavy duty industrial gas turbine control; Identification; Rowen model; feedforward neural network; mechanical behavior; modeling; Data models; Fuels; Mathematical model; Neural networks; Temperature control; Temperature distribution; Turbines; Governor; Heavy duty gas turbine; Neural networks; Rowen´s model; System identification;
Conference_Titel :
Mechatronics and Automation (ICMA), 2013 IEEE International Conference on
Conference_Location :
Takamatsu
Print_ISBN :
978-1-4673-5557-5
DOI :
10.1109/ICMA.2013.6617986
