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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/104572

    Title: Sensorless wind energy conversion system maximum power pointtracking using Takagi–Sugeno fuzzy cerebellar model articulation control
    Authors: Liu, Peter;Yang, Wen-Tsung;Yang, Chang-En;Hsu, Chia-Lien
    Keywords: WECS;CMAC;T-S fuzzy;Linear matrix inequalities
    Date: 2015-04-01
    Issue Date: 2016-01-06 11:03:30 (UTC+8)
    Abstract: In this paper, we propose a sensorless wind energy conversion system (WECS) maximum wind power point tracking using Takagi–Sugeno fuzzy cerebellar model articulation control (T-S CMAC). The main objective of the WECS is to achieve maximum power transfer under various wind speeds without actual measurement of the wind velocity. We first represent the WECS, which uses a permanent magnet synchronous generator (PMSG), as a nonlinear dynamical model. To carry out the T-S CMAC design, we rewrite the WECS model as a T-S fuzzy representation. The T-S CMAC design is inspired by the architectural similarity of the T-S fuzzy control and CMAC where accordingly the PDC design control gains and weighting parameter are augmented into a single vector. The advantages of this approach are 3-fold: (i) increases accuracy of CMAC initial weights – we assign the initial weights of CMAC using the control gains solved by the LMIs from the PDC design; (ii) introduces adaptive ability in LMI-based design – the CMAC design allows time-varying parameters in the system; and (iii) relaxes assumption on system uncertainty – we drop the assumption that a strict upper bound on system uncertainty is known. Numerical simulations under various wind speeds show exponential convergence results which further verify the theoretical derivations.
    Relation: Applied Soft Computing 29, pp.450-460
    DOI: 10.1016/j.asoc.2015.01.019
    Appears in Collections:[電機工程學系暨研究所] 期刊論文

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