Item-based Reliability-centred Life-Cycle Costing using Monte Carlo Simulation

This paper presents a time-sequential probabilistic simulation model for the detailed design of maintenance strategies for turbine critical items. The term item shall refer to any part, component, device, subsystem, or functional unit of a wind turbine that can be individually described and considered. The model enables wind farm operators and turbine manufactures to find the most cost-effective maintenance strategy for each turbine critical item. Cost optimizations are realized through a better adaptation of the maintenance strategy to the item-specific failure modes, degradation processes, failure detection capabilities and the given operational configuration of the wind farm. Based on a time-sequential Monte Carlo simulation technique, the maintenance activities at turbine level are simulated over the windfarm's operational lifetime, considering correlations between the stochastic variables. The results of the Monte Carlo simulation are evaluated using statistical means, thereby, determining the optimal maintenance strategy and associated parameters. The developed model is implemented as a Python application and equally applicable for onshore and offshore windfarms.