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research-article

MAINTENANCE PLANNING OF AN OFFSHORE WIND TURBINE USING STOCHASTIC PETRI NETS WITH PREDICATES

[+] Author and Article Information
Fernando P Santos

Centre for Marine Technology and Ocean Engineering (CENTEC), Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, nº 1, 1049-001 Lisboa, Portugal
fernando.santos@mar.ist.utl.pt

A.P. Teixeira

Centre for Marine Technology and Ocean Engineering (CENTEC), Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, nº 1, 1049-001 Lisboa, Portugal
teixeira@centec.tecnico.ulisboa.pt

Carlos Guedes Soares

Centre for Marine Technology and Ocean Engineering (CENTEC), Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, nº 1, 1049-001 Lisboa, Portugal
c.guedes.soares@centec.tecnico.ulisboa.pt

1Corresponding author.

ASME doi:10.1115/1.4038934 History: Received September 13, 2014; Revised December 07, 2017

Abstract

Operations and maintenance activities have a significant impact on the energy cost for offshore wind turbines. Analytical methods such as reliability block diagrams and Markov processes along with simulation approaches have been widely used in planning and optimizing operations and maintenance actions in industrial systems. Generalized stochastic Petri nets with predicates coupled with Monte Carlo simulation are applied in this paper to model the planning of operations and maintenance activities of an offshore wind turbine. The merits of generalized stochastic Petri nets in modelling complex and multi-component systems are addressed. Three maintenance categories classified according to the size and weight of the components to be replaced and the logistics involved, such as vessels, maintenance crew and spares and, the associated delays and costs are included in the model. The weather windows for accessing the wind turbine are also modelled. Corrective maintenance based on replacements and age-dependent preventive maintenance with imperfect repair are modelled and compared in terms of the wind turbine's performance (e.g. availability and loss production) and of the operations and maintenance costs.

Copyright (c) 2017 by ASME
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