Modelling and simulation of operation and maintenance strategy for offshore wind farms based on multi-agent system

M’hammed Sahnoun (), David Baudry (), Navonil Mustafee (), Anne Louis (), Philip Andi Smart (), Phil Godsiff () and Belahcene Mazari ()
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M’hammed Sahnoun: CESI – IRISE Laboratory
David Baudry: CESI – LUSINE Laboratory
Navonil Mustafee: University of Exeter
Anne Louis: CESI – IRISE Laboratory
Philip Andi Smart: University of Exeter
Phil Godsiff: University of Exeter
Belahcene Mazari: CESI – IRISE Laboratory

Journal of Intelligent Manufacturing, 2019, vol. 30, issue 8, No 13, 2997 pages

Abstract: Abstract Maintenance of offshore wind turbines is a complex and costly undertaking which acts as a barrier to the development of this source of energy. Factors such as the size of the turbines, the size of the wind farms, their distance from the coast and meteorological conditions make it difficult for the stakeholders to select the optimal maintenance strategy. With the objective of reducing costs and duration of such operations it is important that new maintenance techniques are investigated. In this paper we propose a hybrid model of maintenance that is based on multi-agent systems; this allows for the modelling of systems with dynamic interactions between multiple parts. A multi-criteria decision algorithm has been developed to allow analysis and selection of different maintenance strategies. A cost model that includes maintenance action cost, energy loss and installation of monitoring system cost has been presented. For the purposes of this research we have developed a simulator using NetLogo software and have provided experimental results. The results show that employing the proposed hybrid maintenance strategy could increase wind farm productivity and reduce maintenance cost.

Keywords: Offshore wind turbine; Renewable energy; Maintenance; Failure modes; Multi-agent systems; Simulation (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (8)

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DOI: 10.1007/s10845-015-1171-0

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