Research Papers: Gas Turbines: Turbomachinery

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

[+] Author and Article Information
Hamid Asgari


XiaoQi Chen

Department of Mechanical Engineering,
University of Canterbury,
Christchurch 8140, New Zealand

Mohammad B. Menhaj

Department of Electrical Engineering,
Amir Kabir University of Technology,
Tehran, Iran

Raazesh Sainudiin

Department of Mathematics and Statistics,
University of Canterbury,
Christchurch 8140, New Zealand

Contributed by the Turbomachinery Committee of ASME for publication in the Journal of Engineering for Gas Turbines and Power. Manuscript received April 29, 2013; final manuscript received May 8, 2013; published online July 31, 2013. Editor: David Wisler.

J. Eng. Gas Turbines Power 135(9), 092601 (Jul 31, 2013) (7 pages) Paper No: GTP-13-1118; doi: 10.1115/1.4024735 History: Received April 29, 2013; Revised May 08, 2013

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.

Copyright © 2013 by ASME
Your Session has timed out. Please sign back in to continue.


Asgari, H., Chen, X. Q., Menhaj, M. B., and Sainudiin, R., 2012, “ANN-Based System Identification, Modelling and Control of Gas Turbines—A Review,” Adv. Mater. Res., 622–623, pp. 611–617. [CrossRef]
Chiras, N., Evans, C., and Rees, D., 2001, “Nonlinear Gas Turbine Modelling Using NARMAX Structures,” IEEE Trans. Instrum. Meas., 50(4), pp. 893–898. [CrossRef]
Chiras, N., Evans, C., and Rees, D., 2002, “Nonlinear Modelling and Validation of an Aircraft Gas Turbine Engine,” Nonlinear Control Systems 2001 (IFAC Symposia Series), A. B.Kuržanskij, A. L.Fradkov, eds., Pergamon, pp. 871–876.
Chiras, N., Evans, C., and Rees, D., 2002, “Nonlinear Gas Turbine Modelling Using Feedforward Neural Networks,” ASME Turbo Expo 2002: Power for Land, Sea, and Air, Amsterdam, The Netherlands, June 3–6, ASME Paper No. GT2002-30035, pp. 145–152. [CrossRef]
Chiras, N., Evans, C., and Rees, D., 2002, “Global Nonlinear Modelling of Gas Turbine Dynamics Using NARMAX Structures,” ASME J. Eng. Gas Turbines Power, 124, pp. 817–826. [CrossRef]
Ruano, A. E., Fleming, P. J., Teixeira, C., Rodríguez-Vázquez, K. R., and Fonseca, C. M., 2003, “Nonlinear Identification of Aircraft Gas Turbine Dynamics,” Neurocomputing, 55, pp. 551–579. [CrossRef]
Torella, G., Gamma, F., and Palmesano, G., 2003, “Neural Networks for the Study of Gas Turbine Engines Air System,” Proceedings of the International Gas Turbine Congress, Tokyo, Japan, November 2–7.
Lazzaretto, A., and Toffolo, A., 2001, “Analytical and Neural Network Models for Gas Turbine Design and Off-Design Simulation,” Int. J. Thermodyn., 4(4), pp. 173–182, available at: http://ijoticat.com/index.php/IJoT/article/viewArticle/78
Jurado, F., 2005, “Nonlinear Modelling of Microturbines Using NARX Structures on the Distribution Feeder,” Energy Convers. Manage., 46, pp. 385–401. [CrossRef]
Bartolini, C. M., Caresana, F., Comodi, G., Pelagalli, L., Renzi, M., and Vagni, S., 2011, “Application of Artificial Neural Networks to Micro Gas Turbines,” Energy Convers. Manage., 52, pp. 781–788. [CrossRef]
Bettocchi, R., Pinelli, M., Spina, P. R., Venturini, M., and Burgio, M., 2004, “Set Up of a Robust Neural Network for Gas Turbine Simulation,” ASME Turbo Expo 2004, Vienna, Austria, June 14–17, ASME Paper No. GT2004-53421, pp. 543–551. [CrossRef]
Bettocchi, R., Pinelli, M., Spina, P. R., and Venturini, M., 2005, “Artificial Intelligent for the Diagnostics of Gas Turbines: Part 1—Neural Network Approach,” ASME Turbo Expo 2005, Reno, NV, June 6–9, ASME Paper No. GT2005-68026, pp. 9–18. [CrossRef]
Basso, M., Giarre, L., Groppi, S., and Zappa, G., 2004, “NARX Models of an Industrial Power Plant Gas Turbine,” IEEE Trans. Control Syst. Technol., 13, pp. 599–604. [CrossRef]
Yoru, Y., Karakoc, T. H., and Hepbasli, A., 2009, “Application of Artificial Neural Network (ANN) Method to Exergetic Analyses of Gas Turbines,” International Symposium on Heat Transfer in Gas Turbine Systems, Antalya, Turkey, August 9–14.
Simani, S., and Patton, R., 2008, “Fault Diagnosis of an Industrial Gas Turbine Prototype Using a System Identification Approach,” Control Eng. Pract., 16, pp. 769–786. [CrossRef]
Fast, M., Assadi, M., and De, S., 2008, “Condition Based Maintenance of Gas Turbines Using Simulation Data and Artificial Neural Network: A Demonstration of Feasibility,” ASME Turbo Expo 2008, Berlin, Germany, June 9–13, ASME Paper No. GT2008-50768, pp. 153–161 [CrossRef].
Fast, M., Assadi, M., and De, S., 2009, “Development and Multi-Utility of an ANN Model for an Industrial Gas Turbine,” J. Appl. Energy, 86(1), pp. 9–17. [CrossRef]
Fast, M., Palme, T., and Genrup, M., 2009, “A Novel Approach for Gas Turbine Monitoring Combining CUSUM Technique and Artificial Neural Network,” ASME Turbo Expo 2009, Orlando, FL, June 8–12, ASME Paper No. GT2009-59402, pp. 567–574. [CrossRef]
Fast, M., Palme, T., and Karlsson, A., 2009, “Gas Turbines Sensor Validation Through Classification With Artificial Neural Networks,” ECOS 2009, Foz do Iguaçú, Brazil, August 31–September 3.
Fast, M., and Palme, T., 2010, “Application of Artificial Neural Network to the Condition Monitoring and Diagnosis of a Combined Heat and Power Plant,” J. Energy, 35(2), pp. 1114–1120. [CrossRef]
Fast, M., 2010, “Artificial Neural Networks for Gas Turbine Monitoring,” Ph.D. thesis, Division of Thermal Power Engineering, Department of Energy Sciences, Faculty of Engineering, Lund University, Lund, Sweden.
Spina, P. R., and Venturini, M., 2007, “Gas Turbine Modelling by Using Neural Networks Trained on Field Operating Data,” ECOS 2007, Padova, Italy, June 25–28.
Ogaji, S. O. T., Singh, R., and Probert, S. D., 2002, “Multiple-Sensor Fault-Diagnosis for a 2-Shaft Stationary Gas Turbine,” Appl. Energy, 71, pp. 321–339. [CrossRef]
Arriagada, J., Genrup, M., Loberg, A., and Assadi, M., 2003, “Fault Diagnosis System for an Industrial Gas Turbine by Means of Neural Networks,” Proceedings of the International Gas Turbine Congress 2003, Tokyo, Japan, November 2–7.
Ailer, P., Santa, I., Szederkenyi, G., and Hangos, K. M., 2002, “Nonlinear Model-Building of a Low-Power Gas Turbine,” Period. Polytech., Transp. Eng., 29(1–2), pp. 117–135, available at: http://eprints.sztaki.hu/id/eprint/2872
Bank Tavakoli, M. R., Vahidi, B., and Gawlik, W., 2009, “An Educational Guide to Extract the Parameters of Heavy-Duty Gas Turbines Model in Dynamic Studies Based on Operational Data,” IEEE Trans. Power Syst., 24(3), pp. 1366–1374. [CrossRef]
Cybenko, G., 1989, “Approximation by Superpositions of a Sigmoidal Function,” Math. Control, Signals, Syst., 2, pp. 303–314. [CrossRef]


Grahic Jump Location
Fig. 1

A schematic of a typical single-shaft gas turbine

Grahic Jump Location
Fig. 2

Typical Brayton cycle in temperature-entropy frame [26]

Grahic Jump Location
Fig. 3

Simplified SIMULINK model of the gas turbine

Grahic Jump Location
Fig. 4

A schematic of the ANN structure for the gas turbine engine

Grahic Jump Location
Fig. 5

Flow diagram of the generated computer code for ANN-based system identification of the gas turbine

Grahic Jump Location
Fig. 6

Performance of the optimal ANN

Grahic Jump Location
Fig. 7

Regression of the optimal ANN

Grahic Jump Location
Fig. 8

Comparison between outputs of the SIMULINK model and the optimal ANN model for gas turbine rotational speed

Grahic Jump Location
Fig. 9

Comparison between outputs of the SIMULINK model and ANN model for gas turbine temperatures



Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In