Model predictive control framework for optimizing offshore wind O&M

M. Borsotti*, R.R. Negenborn, X. Jiang

*Corresponding author for this work

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

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Abstract

Offshore wind farms are a promising source of renewable energy, but they face significant challenges in terms of operation and maintenance (O&M). Traditional scheduling models often overlook the potential of condition-based maintenance (CBM). Addressing this gap, this paper introduces a novel framework, incorporating principles of Model Predictive Control (MPC), to optimize the O&M scheduling of offshore wind farms using prognostic-driven maintenance. The framework integrates probabilistic remaining useful life (RUL) prognosis in a mixed-integer linear programming (MILP) optimization model with a rolling horizon approach, in alignment with MPC’s predictive and adaptive decision-making approach. The optimization model determines the optimal time to replace each component by minimizing the expected cost over the expected lifetime. This approach seeks to achieve the lowest expense while guaranteeing the highest utilization rate of each component. For the case study presented, the total O&M costs are reduced by up to 15% with respect to corrective maintenance strategies.
Original languageEnglish
Title of host publicationAdvances in Maritime Technology and Engineering
EditorsC. Guedes Soares, Tiago A. Santos
PublisherTaylor & Francis
Pages533-546
Number of pages14
Volume1
ISBN (Electronic)9781003508762
ISBN (Print)9781032833279
DOIs
Publication statusPublished - 2024
EventMARTECH 2024 - Lisbon, Portugal - Istituto Tecnico Superior, Lisbon, Portugal
Duration: 14 May 202417 May 2024
http://www.centec.tecnico.ulisboa.pt/martech2024/

Publication series

NameAdvances in Maritime Technology and Engineering
Volume1

Conference

ConferenceMARTECH 2024 - Lisbon, Portugal
Country/TerritoryPortugal
CityLisbon
Period14/05/2417/05/24
Internet address

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

  • Operation and Maintenance
  • Offshore Wind Energy
  • Prognostic
  • Remaining Useful Life prediction
  • Maintenance scheduling

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