YaBeSH Engineering and Technology Library

    • Journals
    • PaperQuest
    • YSE Standards
    • YaBeSH
    • Login
    View Item 
    •   YE&T Library
    • ASME
    • Journal of Engineering for Gas Turbines and Power
    • View Item
    •   YE&T Library
    • ASME
    • Journal of Engineering for Gas Turbines and Power
    • View Item
    • All Fields
    • Source Title
    • Year
    • Publisher
    • Title
    • Subject
    • Author
    • DOI
    • ISBN
    Advanced Search
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Archive

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

    Source: Journal of Engineering for Gas Turbines and Power:;2013:;volume( 135 ):;issue: 009::page 92601
    Author:
    Asgari, Hamid
    ,
    Chen, XiaoQi
    ,
    Menhaj, Mohammad B.
    ,
    Sainudiin, Raazesh
    DOI: 10.1115/1.4024735
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: 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 lowpower 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 feedforward 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 twolayer 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 singleshaft 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.
    • Download: (1.657Mb)
    • Show Full MetaData Hide Full MetaData
    • Get RIS
    • Item Order
    • Go To Publisher
    • Price: 5000 Rial
    • Statistics

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

    URI
    http://yetl.yabesh.ir/yetl1/handle/yetl/151686
    Collections
    • Journal of Engineering for Gas Turbines and Power

    Show full item record

    contributor authorAsgari, Hamid
    contributor authorChen, XiaoQi
    contributor authorMenhaj, Mohammad B.
    contributor authorSainudiin, Raazesh
    date accessioned2017-05-09T00:58:28Z
    date available2017-05-09T00:58:28Z
    date issued2013
    identifier issn1528-8919
    identifier othergtp_135_09_092601.pdf
    identifier urihttp://yetl.yabesh.ir/yetl/handle/yetl/151686
    description abstractDuring 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 lowpower 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 feedforward 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 twolayer 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 singleshaft 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.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleArtificial Neural Network–Based System Identification for a Single Shaft Gas Turbine
    typeJournal Paper
    journal volume135
    journal issue9
    journal titleJournal of Engineering for Gas Turbines and Power
    identifier doi10.1115/1.4024735
    journal fristpage92601
    journal lastpage92601
    identifier eissn0742-4795
    treeJournal of Engineering for Gas Turbines and Power:;2013:;volume( 135 ):;issue: 009
    contenttypeFulltext
    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
    yabeshDSpacePersian
     
    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
    yabeshDSpacePersian