Faedo, Nicolás, 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 |
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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) |
Related URLs: | |
URI: | https://mu.eprints-hosting.org/id/eprint/14300 |
Use Licence: | This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here |
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