Research Papers: Ocean Renewable Energy

Maintenance Planning of an Offshore Wind Turbine Using Stochastic Petri Nets With Predicates

[+] Author and Article Information
F. P. Santos

Centre for Marine Technology and Ocean
Engineering (CENTEC),
Instituto Superior Técnico,
Universidade de Lisboa,
Av. Rovisco Pais, n° 1,
Lisboa 1049-001, Portugal
e-mail: fernando.santos@centec.tecnico.ulisboa.pt

A. P. Teixeira

Centre for Marine Technology and Ocean
Engineering (CENTEC),
Instituto Superior Técnico,
Universidade de Lisboa,
Av. Rovisco Pais, n° 1,
Lisboa 1049-001, Portugal
e-mail: teixeira@centec.tecnico.ulisboa.pt

C. Guedes Soares

Centre for Marine Technology and Ocean
Engineering (CENTEC),
Instituto Superior Técnico,
Universidade de Lisboa,
Av. Rovisco Pais, n° 1,
Lisboa 1049-001, Portugal
e-mail: c.guedes.soares@centec.tecnico.ulisboa.pt

1Corresponding author.

Contributed by the Ocean, Offshore, and Arctic Engineering Division of ASME for publication in the JOURNAL OF OFFSHORE MECHANICS AND ARCTIC ENGINEERING. Manuscript received September 13, 2014; final manuscript received December 7, 2017; published online February 8, 2018. Editor: Lance Manuel.

J. Offshore Mech. Arct. Eng 140(2), 021904 (Feb 08, 2018) (9 pages) Paper No: OMAE-14-1128; doi: 10.1115/1.4038934 History: Received September 13, 2014; Revised December 07, 2017

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 (GSPNs) with predicates coupled with Monte Carlo simulation (MCS) are applied in this paper to model the planning of operations and maintenance activities of an offshore wind turbine. The merits of GSPN in modeling complex and multicomponent 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 modeled. Corrective maintenance (CM) based on replacements and age-dependent preventive maintenance (PM) with imperfect repair are modeled and compared in terms of the wind turbine's performance (e.g., availability and loss production) and of the operations and maintenance costs.

Copyright © 2018 by ASME
Topics: Maintenance , Vessels
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Grahic Jump Location
Fig. 1

Petri net model of a simple repairable system [26]

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Fig. 2

Failure rates of the onshore turbine's components

Grahic Jump Location
Fig. 7

GSPN for the weather forecast

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Fig. 6

GSPN of the maintenance crew and supply vessel

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Fig. 5

GSPN of the jack-up vessel mobilization/demobilization

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Fig. 4

GSPN model of the rotor's CM

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Fig. 3

GSPN for the seasons of the year

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Fig. 8

GSPN model of the rotor's PM



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