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Energies 2017, 10(4), 472;

System Identification of a Heaving Point Absorber: Design of Experiment and Device Modeling

Sandia National Laboratories, Albuquerque, NM 87123, USA
Author to whom correspondence should be addressed.
Academic Editor: Aristides Kiprakis
Received: 2 January 2017 / Revised: 3 March 2017 / Accepted: 15 March 2017 / Published: 3 April 2017
(This article belongs to the Special Issue Marine Energy)


Empirically based modeling is an essential aspect of design for a wave energy converter. Empirically based models are used in structural, mechanical and control design processes, as well as for performance prediction. Both the design of experiments and methods used in system identification have a strong impact on the quality of the resulting model. This study considers the system identification and model validation process based on data collected from a wave tank test of a model-scale wave energy converter. Experimental design and data processing techniques based on general system identification procedures are discussed and compared with the practices often followed for wave tank testing. The general system identification processes are shown to have a number of advantages, including an increased signal-to-noise ratio, reduced experimental time and higher frequency resolution. The experimental wave tank data is used to produce multiple models using different formulations to represent the dynamics of the wave energy converter. These models are validated and their performance is compared against one another. While most models of wave energy converters use a formulation with surface elevation as an input, this study shows that a model using a hull pressure measurement to incorporate the wave excitation phenomenon has better accuracy. View Full-Text
Keywords: wave energy; system identification; model validation; wave tank testing wave energy; system identification; model validation; wave tank testing

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Bacelli, G.; Coe, R.G.; Patterson, D.; Wilson, D. System Identification of a Heaving Point Absorber: Design of Experiment and Device Modeling. Energies 2017, 10, 472.

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