Hamid Asgari

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

—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 environment for creating and training different ANN models with two-layer feed-forward multi-layer 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 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 18720 ANN models for system identification of single shaft GT. 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.

Modelling and simulation of gas turbines

Today, gas turbines (GTs) are one of the major parts of modern industry. They have played a key role in aeronautical industry, power generation, and main mechanical drivers for large pumps and compressors. Modelling and simulation of GTs has always been a powerful tool for performance optimisation of this kind of equipment. Remarkable research activities have been carried out in this field and a variety of analytical and experimental models have been built so far to get in-depth understanding of the non-linear behaviour and complex dynamics of these systems. However, the need to develop accurate and reliable models of gas turbines for different objectives and applications has been a strong motivation for researchers to continue to work in this area. This paper focuses on major research activities which have been carried out so far in the field of modelling and simulation of gas turbines. It covers main white-box and black-box models and their applications in control systems. This study can be a good reference for current and prospective researchers who are working or planning to work in this fascinating area of research.

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

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.

Gas Turbines Modeling, Simulation, and Control: Using Artificial Neural Networks

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Design of conventional and neural network based controllers for a single-shaft gas turbine

Purpose The purpose of this paper is to develop and compare conventional and neural network-based controllers for gas turbines. Design/methodology/approach Design of two different controllers is considered. These controllers consist of a NARMA-L2 which is an artificial neural network-based nonlinear autoregressive moving average (NARMA) controller with feedback linearization, and a conventional proportional-integrator-derivative (PID) controller for a low-power aero gas turbine. They are briefly described and their parameters are adjusted and tuned in Simulink-MATLAB environment according to the requirement of the gas turbine system and the control objectives. For this purpose, Simulink and neural network-based modelling is used. Performances of the controllers are explored and compared on the base of design criteria and performance indices. Findings It is shown that NARMA-L2, as a neural network-based controller, has a superior performance to PID controller. Practical implications This study aims at using artificial intelligence in gas turbine control systems. Originality/value This paper provides a novel methodology for control of gas turbines.

Modeling and Simulation of the Start-Up Operation of a Heavy-Duty Gas Turbine by Using NARX Models

In this study, nonlinear autoregressive exogenous (NARX) models of a heavy-duty single-shaft gas turbine (GT) are developed and validated. The GT is a power plant gas turbine (General Electric PG 9351FA) located in Italy.The data used for model development are three time series data sets of two different maneuvers taken experimentally during the start-up procedure. The resulting NARX models are applied to three other experimental data sets and comparisons are made among four significant outputs of the models and the corresponding measured data.The results show that NARX models are capable of satisfactory prediction of the GT behavior and can capture system dynamics during start-up operation.Copyright