On the sensitivity and uncertainty of wave energy conversion with an artificial neural-network-based controller

Research output: Contribution to journalArticle

4 Citations (Scopus)
4 Downloads (Pure)

Abstract

This work addresses with sensitivity and uncertainty of the energy conversion of an oscillation-body wave energy converter with an artificial neural-network-based controller. The smart controller applies the model predictive control strategy to implement real-time latching control to the wave energy converter. Since the control inputs are future wave forces, an artificial neural network is developed and trained by the machine learning algorithm to predict the short-term wave forces based on the real-time measurement of wave elevation. The sensitivity of wave energy conversion with respect to wave frequency and receding horizon length are investigated. Uncertainties of the neural network that lead to the prediction deviation are identified and quantified, and their influences on the energy conversion are examined. The control command is derived inappropriately in the presence of prediction deviation leading to the reduction of energy absorption. Moreover, it is the phase deviation that reduces the energy absorption.

Original languageEnglish
Pages (from-to)282-293
Number of pages12
JournalOcean Engineering
Volume183
Early online date16 May 2019
DOIs
Publication statusPublished - 1 Jul 2019

Keywords

  • artificial intelligence
  • artificial neural network
  • machine learning
  • model predictive control
  • wave energy
  • wave force prediction

Fingerprint Dive into the research topics of 'On the sensitivity and uncertainty of wave energy conversion with an artificial neural-network-based controller'. Together they form a unique fingerprint.

  • Projects

    Research Output

    • 4 Citations
    • 2 Article

    Wave force prediction effect on the energy absorption of a wave energy converter with real-time control

    Li, L., Yuan, Z., Gao, Y. & Zhang, X., 30 Apr 2019, In : IEEE Transactions on Sustainable Energy. 10, 2, p. 615-624 10 p.

    Research output: Contribution to journalArticle

    Open Access
    File
  • 6 Citations (Scopus)
    56 Downloads (Pure)

    Maximization of energy absorption for a wave energy converter using the deep machine learning

    Li, L., Yuan, Z. & Gao, Y., 15 Dec 2018, In : Energy. 165, Part A, p. 340-349 10 p.

    Research output: Contribution to journalArticle

    Open Access
    File
  • 11 Citations (Scopus)
    6 Downloads (Pure)

    Activities

    • 1 Visiting an external academic institution

    Norwegian University of Science and Technology (NTNU)

    Liang Li (Visiting researcher)

    1 Jul 20181 Sep 2018

    Activity: Visiting an external institution typesVisiting an external academic institution

    Cite this