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    Smoothing of wind farm output by prediction and supervisory-control-unit- based FESS

    245854 Wind farm.pdf (1.573Mb)
    Access Status
    Open access
    Authors
    Islam, F.
    Al-Durra, A.
    Muyeen, S.M.
    Date
    2013
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Islam, F. and Al-Durra, A. and Muyeen, S.M. 2013. Smoothing of wind farm output by prediction and supervisory-control-unit- based FESS. IEEE Transactions on Sustainable Energy. 4 (4): pp. 925-933.
    Source Title
    IEEE Transactions on Sustainable Energy
    DOI
    10.1109/TSTE.2013.2256944
    ISSN
    1949-3029
    School
    Department of Electrical and Computer Engineering
    Remarks

    © 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

    URI
    http://hdl.handle.net/20.500.11937/18118
    Collection
    • Curtin Research Publications
    Abstract

    This paper presents a supervisory control unit (SCU) combined with short-term ahead wind speed prediction for proper and effective management of the stored energy in a small capacity flywheel energy storage system (FESS) which is used to mitigate the output power fluctuations of an aggregated wind farm. Wind speed prediction is critical for a wind energy conversion system since it may greatly influence the issues related to effective energy management, dynamic control of wind turbine, and improvement of the overall efficiency of the power generation system. In this study, a wind speed prediction model is developed by artificial neural network (ANN) which has advantages over the conventional prediction schemes including data error tolerance and ease in adaptability. The proposed SCU-based control would help to reduce the size of the energy storage system for minimizing wind power fluctuation taking the advantage of prediction scheme. The model for prediction using ANN is developed in MATLAB/Simulink and interfaced with PSCAD/EMTDC. Effectiveness of the proposed control system is illustrated using real wind speed data in various operating conditions.

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