Artificial Neural Network-Based System Identification for a Single-Shaft Gas Turbine

被引:52
作者
Asgari, Hamid [1 ]
Chen, XiaoQi [1 ]
Menhaj, Mohammad B. [2 ]
Sainudiin, Raazesh [3 ]
机构
[1] Univ Canterbury, Dept Mech Engn, Christchurch 8140, New Zealand
[2] Amirkabir Univ Technol, Dept Elect Engn, Tehran, Iran
[3] Univ Canterbury, Dept Math & Stat, Christchurch 8140, New Zealand
来源
JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER-TRANSACTIONS OF THE ASME | 2013年 / 135卷 / 09期
关键词
gas turbine; neural network; system identification; modeling; simulation; optimization; MODEL;
D O I
10.1115/1.4024735
中图分类号
TH [机械、仪表工业];
学科分类号
0802 ;
摘要
During recent decades, artificial intelligence has been employed as a powerful tool for identification of complex industrial systems with nonlinear dynamics, such as gas turbines (GT). In this study, a methodology based on artificial neural network (ANN) techniques was developed for offline system identification of a low-power gas turbine. The processed data was obtained from a SIMULINK model of a gas turbine in MATLAB environment. A comprehensive computer program code was generated and run in MATLAB for creating and training different ANN models with feed-forward multilayer perceptron (MLP) structure. The code consisted of various training functions, different number of neurons as well as a variety of transfer (activation) functions for hidden and output layers of the network. It was shown that the optimal model for a two-layer network with MLP structure consisted of 20 neurons in its hidden layer and used trainlm as its training function, as well as tansig and logsid as its transfer functions for the hidden and output layers. It was also observed that trainlm has a superior performance in terms of minimum mean squared error (MSE) compared with each of the other training functions. The resulting model could predict performance of the system with high accuracy. The methodology provides a comprehensive view of the performance of over 18,720 ANN models for system identification of the single-shaft gas turbine. One can use the optimal ANN model from this study when training from real data obtained from this type of GT. This is particularly useful when real data is only available over a limited operational range.
引用
收藏
页数:7
相关论文
共 27 条
[1]  
[Anonymous], 2002, PERIODICA POLYTECHNI
[2]  
Arriagada J., 2003, P INT GAS TURB C 200
[3]  
Asgari Hamid, 2013, Advanced Materials Research, V622-623, P611, DOI 10.4028/www.scientific.net/AMR.622-623.611
[4]   Application of artificial neural networks to micro gas turbines [J].
Bartolini, C. M. ;
Caresana, F. ;
Comodi, G. ;
Pelagalli, L. ;
Renzi, M. ;
Vagni, S. .
ENERGY CONVERSION AND MANAGEMENT, 2011, 52 (01) :781-788
[5]   NARX models of an industrial power plant gas turbine [J].
Basso, M ;
Giarré, L ;
Groppi, S ;
Zappa, G .
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, 2005, 13 (04) :599-604
[6]  
Bettocchi R, 2005, PROCEEDINGS OF THE ASME TURBO EXPO 2005, VOL 4, P9
[7]  
Bettocchi R., 2004, ASME TURB EXP 2004 V, P543
[8]  
Chiras N, 2002, IFAC SYMP SERIES, P871
[9]   Global nonlinear modeling of gas turbine dynamics using NARMAX structures [J].
Chiras, N ;
Evans, C ;
Rees, D .
JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER-TRANSACTIONS OF THE ASME, 2002, 124 (04) :817-826
[10]   Nonlinear gas turbine modeling using NARMAX structures [J].
Chiras, N ;
Evans, C ;
Rees, D .
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2001, 50 (04) :893-898