YaBeSH Engineering and Technology Library

    • Journals
    • PaperQuest
    • YSE Standards
    • YaBeSH
    • Login
    View Item 
    •   YE&T Library
    • ASME
    • Journal of Electrochemical Energy Conversion and Storage
    • View Item
    •   YE&T Library
    • ASME
    • Journal of Electrochemical Energy Conversion and Storage
    • 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

    Study on Nonlinear Identification SOFC Temperature Model Based on Particle Swarm Optimization–Least-Squares Support Vector Regression

    Source: Journal of Electrochemical Energy Conversion and Storage:;2017:;volume( 014 ):;issue: 003::page 31003
    Author:
    Chen, Jinwei
    ,
    Zhang, Huisheng
    ,
    Weng, Shilie
    DOI: 10.1115/1.4036805
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: In order to facilitate valid solid oxide fuel cell (SOFC) temperature control scheme, a nonlinear identification method of SOFC temperature dynamic behaviors is proposed using an autoregressive network with exogenous inputs (NARX) model, whose nonlinear function is described by a least-squares support vector regression (LSSVR) method with radial basis kernel function (RBF). During the identifying process, a particle swarm optimization (PSO) algorithm is introduced to optimize the parameters of LSSVR. On the other hand, a mechanism model is developed to sample the training data to regress the NARX model. Investigations are conducted to analyze the effects of training data size and PSO fitness function on the accuracy of the NARX model. The results demonstrate that the NARX model with tenfold cross-validation fitness function and large size data is precise enough in predicting the SOFC temperature dynamic behaviors. The maximum errors of cathode and anode outlet temperature are 0.3081 K and 0.3293 K, respectively. Furthermore, the simulation speed of NARX model is much faster than the mechanism model because NARX model avoids the internal complex computation process. The training time of the NARX model with large size data is about 1.2 s. For a 20,000 s simulation, the predicting time of the NARX model is about 0.2 s, while the mechanism model is about 36 s. In consideration of its high computational speed and accuracy, NARX model is a powerful candidate for valid multivariable model predictive control (MPC) schemes.
    • Download: (1.067Mb)
    • Show Full MetaData Hide Full MetaData
    • Get RIS
    • Item Order
    • Go To Publisher
    • Price: 5000 Rial
    • Statistics

      Study on Nonlinear Identification SOFC Temperature Model Based on Particle Swarm Optimization–Least-Squares Support Vector Regression

    URI
    http://yetl.yabesh.ir/yetl1/handle/yetl/4236807
    Collections
    • Journal of Electrochemical Energy Conversion and Storage

    Show full item record

    contributor authorChen, Jinwei
    contributor authorZhang, Huisheng
    contributor authorWeng, Shilie
    date accessioned2017-11-25T07:20:59Z
    date available2017-11-25T07:20:59Z
    date copyright2017/21/6
    date issued2017
    identifier issn2381-6872
    identifier otherjeecs_014_03_031003.pdf
    identifier urihttp://138.201.223.254:8080/yetl1/handle/yetl/4236807
    description abstractIn order to facilitate valid solid oxide fuel cell (SOFC) temperature control scheme, a nonlinear identification method of SOFC temperature dynamic behaviors is proposed using an autoregressive network with exogenous inputs (NARX) model, whose nonlinear function is described by a least-squares support vector regression (LSSVR) method with radial basis kernel function (RBF). During the identifying process, a particle swarm optimization (PSO) algorithm is introduced to optimize the parameters of LSSVR. On the other hand, a mechanism model is developed to sample the training data to regress the NARX model. Investigations are conducted to analyze the effects of training data size and PSO fitness function on the accuracy of the NARX model. The results demonstrate that the NARX model with tenfold cross-validation fitness function and large size data is precise enough in predicting the SOFC temperature dynamic behaviors. The maximum errors of cathode and anode outlet temperature are 0.3081 K and 0.3293 K, respectively. Furthermore, the simulation speed of NARX model is much faster than the mechanism model because NARX model avoids the internal complex computation process. The training time of the NARX model with large size data is about 1.2 s. For a 20,000 s simulation, the predicting time of the NARX model is about 0.2 s, while the mechanism model is about 36 s. In consideration of its high computational speed and accuracy, NARX model is a powerful candidate for valid multivariable model predictive control (MPC) schemes.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleStudy on Nonlinear Identification SOFC Temperature Model Based on Particle Swarm Optimization–Least-Squares Support Vector Regression
    typeJournal Paper
    journal volume14
    journal issue3
    journal titleJournal of Electrochemical Energy Conversion and Storage
    identifier doi10.1115/1.4036805
    journal fristpage31003
    journal lastpage031003-10
    treeJournal of Electrochemical Energy Conversion and Storage:;2017:;volume( 014 ):;issue: 003
    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