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    Passivity preserving moment-based finite-order hydrodynamic model identification for wave energy applications


    Faedo, Nicolás and Peña-Sanchez, Yerai and Ringwood, John (2018) Passivity preserving moment-based finite-order hydrodynamic model identification for wave energy applications. In: Advances in Renewable Energies Offshore. Taylor & Francis, pp. 351-359. ISBN 9781138585355

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    Abstract

    The dynamics of a Wave Energy Converter (WEC) are described in terms of an integrodifferential equation, particularly, of the convolution class. This convolution term, which is associated with fluid memory effects of the radiation forces acting on the WEC, represents a major drawback both for simulation, analysis and control design for WECs. Recently, a moment-matching based method to approximate this convolution term by a parametric model was presented in (Faedo et al. 2018). Such a technique allows the computation of a model that can match exactly the frequency response of the original system at a set of chosen frequencies. Though the models computed by this strategy are almost always inherently passive, the proposed method does not specifically ensure passivity, which is one of the main physical properties of the radiation subsystem. This paper describes an extension of the moment-based methodology presented in (Faedo et al. 2018) which guarantees a passive finite-order representation for the radiation kernel based on moment-matching. Moreover, we illustrate the applicability of the method by the means of a numerical example with a particular WEC.

    Item Type: Book Section
    Additional Information: Funding: This material is based upon works supported by Science Foundation Ireland under Grant no. 13/ IA/1886.
    Keywords: Passivity; preserving; moment based; finite order; hydrodynamic; model identification; wave energy; applications;
    Academic Unit: Faculty of Science and Engineering > Research Institutes > Centre for Ocean Energy Research
    Faculty of Science and Engineering > Electronic Engineering
    Item ID: 14300
    Depositing User: Professor John Ringwood
    Date Deposited: 01 Apr 2021 13:40
    Publisher: Taylor & Francis
    Refereed: Yes
    Funders: Science Foundation Ireland (SFI)
    URI:

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