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Impact of condition monitoring on the maintenance and economic viability of offshore wind turbines

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posted on 2023-07-05, 09:10 authored by Rundong Yan, Sarah DunnettSarah Dunnett, Lisa JacksonLisa Jackson

This study explores how condition monitoring (CM) can help operate offshore wind turbines (OWTs) effectively and economically. In this paper, the Petri Net (PN) simulation models are developed to quantitatively assess the OWT availability and operation and maintenance (O&M) costs. By investigating the impact of two CM approaches (i.e. purpose-designed CM and Supervisory Control and Data Acquisition (SCADA)-based CM) and their combinations with various maintenance strategies, the paper addresses two fundamental questions about OWT CM that have plagued the offshore wind sector for many years. They are ‘is a wind farm SCADA system a viable alternative to purpose-designed condition monitoring system (CMS)’ and ‘what is the best way to integrate CMSs and maintenance strategies to maximise the financial benefit of OWTs’. The research suggests that although utilising both a wind farm SCADA system and a purpose-designed CMS can achieve the highest turbine availability, it is not the most cost-effective option in terms of maintenance expenses. Instead, combining purpose-designed CM with less frequent advanced service can achieve the desired availability at the lowest cost. Furthermore, the use of a purpose-designed CMS is essential for the economical operation of OWTs and cannot be replaced by the current wind farm SCADA system.

Funding

Loughborough University

Postdoctoral Fellowship Scheme of the Centre for Postdoctoral Development in Infrastructure Cities and Energy

History

School

  • Aeronautical, Automotive, Chemical and Materials Engineering

Department

  • Aeronautical and Automotive Engineering

Published in

Reliability Engineering & System Safety

Volume

238

Publisher

Elsevier

Version

  • VoR (Version of Record)

Rights holder

© The Authors

Publisher statement

This is an Open Access Article. It is published by Elsevier under the Creative Commons Attribution 4.0 International Licence (CC BY). Full details of this licence are available at: https://creativecommons.org/licenses/by/4.0/

Acceptance date

2023-06-25

Publication date

2023-06-26

Copyright date

2023

ISSN

0951-8320

eISSN

1879-0836

Language

  • en

Depositor

Dr Sarah Dunnett. Deposit date: 1 July 2023

Article number

109475

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