Abstract

Optimal control strategies represent a widespread solution to increase the extracted energy of a Wave Energy Converter (WEC). The aim is to bring the WEC into resonance enhancing the produced power without compromising its reliability and durability. Most of the control algorithms proposed in literature require for the knowledge of the Wave Excitation Force (WEF) generated from the incoming wave field. In practice, WEFs are unknown, and an estimate must be used. This paper investigates the WEF estimation of a non-linear WEC. A model-based and a model-free approach are proposed. First, a Kalman Filter (KF) is implemented considering the WEC linear model and the WEF modelled as an unknown state to be estimated. Second, a feedforward Neural Network (NN) is applied to map the WEC dynamics to the WEF by training the network through a supervised learning algorithm. Both methods are tested for a wide range of irregular sea-states showing promising results in terms of estimation accuracy. Sensitivity and robustness analyses are performed to investigate the estimation error in presence of un-modelled phenomena, model errors and measurement noise.

Original languageEnglish
Article number825
Pages (from-to)1-30
Number of pages30
JournalJournal of Marine Science and Engineering
Volume8
Issue number10
DOIs
Publication statusPublished - 21 Oct 2020

Keywords

  • Estimation
  • Kalman Filter
  • Neural Network
  • Optimal Control
  • Wave Energy Converter
  • Wave excitation forces

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Water Science and Technology
  • Ocean Engineering

Fingerprint Dive into the research topics of 'Real-time wave excitation forces estimation: An application on the ISWEC device'. Together they form a unique fingerprint.

Cite this