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

Operation and maintenance (O&M) 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 O&M actions in industrial systems. Generalized stochastic Petri Nets (GSPN) with predicates coupled with Monte Carlo simulation (MCS) are applied in this paper to model the planning of O&M activities of an offshore wind turbine. The merits of GSPN in modeling complex, multi-state 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 modeled. Corrective maintenance based on replacements and age imperfect preventive maintenance are modeled and compared in terms of the wind turbine's performance (e.g. availability and loss production) and of the O&M costs.