Data-Driven System Identification Modelling for Multi-Float M4 Wave Energy Converter with Elastic Bed-Buoy-Bow Float Mooring

Xuefei Wang, Danni Liang, Mengxiao Li, Peter Stansby, Long Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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Abstract

This paper makes a unique contribution to
apply data-driven system identification methods to model
the mooring of 6-float M4 wave energy converter (WEC).
Within the M4 system, elastic mooring cables connect
the buoy to the basin bed, and the inelastic lines link
the buoy to the bow float. Two linear models including
autoregressive with exogenous inputs (ARX) model and
the Box-Jenkins (BJ) model are used to identify the systems
between three typical features: the wave surface elevation,
the mooring force in the elastics, and the motions of the
bow float. The bow float motions include pitch, surge
and heave. The performance of identified models are
determined from wave basin experiments under a number
of different wave conditions in terms of significant wave
heights and peak wave periods. Different sampling rate
and model orders are also tested during identification. It
is found that for both ARX and BJ models, low orders
and sampling rate are sufficient to identify the M4 WEC
systems.
Original languageEnglish
Title of host publicationPROCEEDINGS OF THE 15TH EUROPEAN WAVE AND TIDAL ENERGY CONFERENCE
Publication statusPublished - Sept 2023

Keywords

  • System Identification
  • M4 Wave Energy Converter
  • Mooring Configuration

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