MURAL - Maynooth University Research Archive Library



    Towards data-driven and data-based control of wave energy systems: Classification, overview, and critical assessment


    Pasta, Edoardo, Faedo, Nicolás, Mattiazzo, Giuliana and Ringwood, John (2023) Towards data-driven and data-based control of wave energy systems: Classification, overview, and critical assessment. Renewable and Sustainable Energy Reviews, 188. p. 113877. ISSN 1364-0321

    [thumbnail of 1-s2.0-S1364032123007359-main.pdf]
    Preview
    Text
    1-s2.0-S1364032123007359-main.pdf

    Download (1MB) | Preview

    Abstract

    Currently, a significant effort in the world research panorama is focused on finding efficient solutions to a carbon-free energy supply, wave energy being one of the most promising sources of untapped renewable energy. However, wave energy is not currently economic, though control technology has been shown to significantly increase the energy capture capabilities. Usually, the synthesis of a wave energy control strategy requires the adoption of control-oriented models, which are prone to error, particularly arising from unmodelled hydrodynamics, given the complexity of the hydrodynamic interactions between the device and the ocean. In this context, data-driven and data-based control strategies provide a potential solution to some of these issues, using real-time data to gather information about the system dynamics and performance. Thus motivated, this study provides a detailed analysis of different approaches to the exploitation of data in the design of control philosophies for wave energy systems, establishing clear definitions of data-driven and databased control in this field, together with a classification highlighting the various roles of data in the control synthesis process. In particular, we investigate intrinsic opportunities and limitations behind the use of data in the process of control synthesis, providing a comprehensive review together with critical considerations aimed at directly contributing towards the development of efficient data-driven and data-based control systems for wave energy devices.
    Item Type: Article
    Keywords: Adaptive control; Data-based control; Data-based modelling; Data-driven control; Energy-maximising control; Learning-based control; Optimal control; Wave energy;
    Academic Unit: Faculty of Science and Engineering > Electronic Engineering
    Faculty of Science and Engineering > Research Institutes > Centre for Ocean Energy Research
    Item ID: 19412
    Identification Number: 10.1016/j.rser.2023.113877
    Depositing User: Professor John Ringwood
    Date Deposited: 24 Jan 2025 14:58
    Journal or Publication Title: Renewable and Sustainable Energy Reviews
    Publisher: Elsevier
    Refereed: Yes
    Related URLs:
    URI: https://mural.maynoothuniversity.ie/id/eprint/19412
    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

    Repository Staff Only (login required)

    Item control page
    Item control page

    Downloads

    Downloads per month over past year

    Origin of downloads