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A Decision-making Model for Corrective Maintenance of Offshore Wind Turbines Considering Uncertainties

descriptionPublicationkeyboard_double_arrow_right Article , Journal , Other literature type 12 Apr 2019Publisher:MDPI AGJournal:Energies, volume 12, page 1,408 (eissn: 1996-1073,

Authors: Sathishkumar Nachimuthu; Ming J. Zuo; Yi Ding;

doi: 10.3390/en12081408

A Decision-making Model for Corrective Maintenance of Offshore Wind Turbines Considering Uncertainties

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Abstract

Maintenance optimization has received special attention among the wind energy research community over the past two decades. This is mainly because of the high degree of uncertainties involved in the execution of operation and maintenance (O&M) activities throughout the lifecycle of wind farms. The increasing complexity in offshore maintenance execution demands applied research and brings forth a need to develop problem-specific maintenance decision-making models. In this paper, a mathematical model is proposed to assist wind farm stakeholders in making critical resource- related decisions for corrective maintenance at offshore wind farms (OWFs), considering uncertainties in turbine failure information.

Related Organizations

Zhejiang Ocean University
China (People's Republic of)
Zhejiang Ocean University
China (People's Republic of)
University of Alberta
Canada

Keywords

Technology, offshore wind farm, T, offshore wind turbine, offshore wind farm; offshore wind turbine; maintenance; failure classification; resource decision; uncertainty, maintenance, failure classification, resource decision, uncertainty

Impact byBIP!

	citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	23
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Top 10%
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Average
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Top 10%