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Research Papers: Gas Turbines: Turbomachinery

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

[+] Author and Article Information
Hamid Asgari

Mem. ASME

XiaoQi Chen

Mem. ASME
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.

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References

Figures

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Fig. 1

A schematic of a typical single-shaft gas turbine

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Fig. 2

Typical Brayton cycle in temperature-entropy frame [26]

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Fig. 3

Simplified SIMULINK model of the gas turbine

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Fig. 4

A schematic of the ANN structure for the gas turbine engine

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Fig. 5

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

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Fig. 6

Performance of the optimal ANN

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Fig. 7

Regression of the optimal ANN

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Fig. 8

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

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Fig. 9

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

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