4 results for Asgari, H.

  • Considerations in Modelling and Control of Gas Turbines - a Review

    Asgari, H.; Chen, X.; Sainudiin, R. (2011)

    Conference Contributions - Published
    University of Canterbury Library

    Modelling and control of gas turbines (GTs) have always been a controversial issue because of the complex dynamics of these kinds of equipment. Considerable research activities have been carried out so far in this field in order to disclose the secrets behind the nonlinear behaviour of these systems. Although the results of the research in this area have been satisfactory so far, it seems that there is no end to the efforts for performance optimization of gas turbines. A variety of analytical and experimental models as well as control systems has been built so far for gas turbines. However, the need for optimized models for different objectives and applications has been a strong motivation for researchers to continue to work in this field. This paper is aimed at presenting a general overview of essential basic criteria that need to be considered for making a satisfactory model and control system of a gas turbine. GT type, GT configuration, modelling methods, modelling objectives as well as control system type and configuration are the main preliminary factors for modelling a gas turbine which will be briefly discussed in the paper. Some of the research in this field will be also stated shortly.

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  • Applications of artificial neural networks (ANNs) to rotating equipment

    Asgari, H.; Chen, X.; Sainudiin, R. (2011)

    Conference Contributions - Published
    University of Canterbury Library

    Rotating equipment is the beating heart of nearly all industrial plants and specifically plays a vital role in oil and power industries. In spite of all research which has been carried out so far to discover accurate dynamics of this kind of equipment, there are still many unknown and unexpected problems on operational sites which need to be solved in order to approach to the optimal operational point of rotary machines. Fortunately, Artificial Neural Networks (ANNs) can provide appropriate solutions to many of these problems. In this paper, a general overview of different applications of ANNs to Industrial rotating equipment is presented. These applications cover a variety of areas including condition monitoring, sensor validation, fault diagnosis, system identification and control. The advantages of using ANNs for each application are discussed briefly by providing practical examples.

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  • NARX models for simulation of the start-up operation of a single-shaft gas turbine

    Asgari, H.; Chen, X.Q.; Morini, M.; Pinelli, M.; Sainudiin, R.; Spina, P.R.; Venturini, M. (2016)

    Journal Articles
    University of Canterbury Library

    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.

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  • Design of Conventional and Neural Network Based Controllers for a Single-Shaft Gas Turbine

    Asgari, H.; Chen, X.Q.; Jegarkandi, M.F.; Sainudiin, R. (2016)

    Journal Articles
    University of Canterbury Library

    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 ANN-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 employed. 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 the PID controller. Practical implications – Using artificial intelligence in gas turbine control systems. Originality/value – Providing a novel methodology for control of gas turbines.

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