ANN-Based System Identification, Modelling and Control of Gas Turbines – A Review

Author:

Asgari Hamid1,Chen Xiao Qi1,Menhaj Mohammad Bagher2,Sainudiin Raazesh1

Affiliation:

1. University of Canterbury

2. Amir Kabir University of Technology

Abstract

Gas Turbines (GTs) are the beating heart of nearly all industrial plants and specifically play a vital role in oil and power industries. Significant research activities have been carried out to discover accurate dynamics and to approach to the optimal operational point of these systems. A variety of analytical and experimental system identification methods, models and control systems has been investigated so far for gas turbines. Artificial neural network (ANN) has been recognized as one of the successful approaches that can disclose nonlinear behaviour of such complicated systems. This paper briefly reviews major ANN-based research activities in the field of system identification, modelling and control of gas turbines. It can be used as a reference for those who are interested to work and study in this area.

Publisher

Trans Tech Publications, Ltd.

Subject

General Engineering

Reference30 articles.

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3. J. Arriagada, M. Genrup, A. Loberg and M. Assadi (2003). Fault Diagnosis System for an Industrial Gas Turbine by Means of Neural Networks., Proceedings of the International Gas Turbine Congress 2003, Tokyo, Japan.

4. M. Basso, L. Giarre, S. Groppi, and G. Zappa (2004). NARX Models of an Industrial Power Plant Gas Turbine., IEEE Transactions on Control Systems Technology.

5. R. Bettochi, M. Pinelli, P. R. Spina, M. Venturini and M. Burgio (2004). Set Up of A Robust Neural Network For Gas Turbine Simulation., ASME Turbo Expo 2004, Vienna, Austria.

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