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and Risø-DTU, DenmarkFor offshore wind turbines, costs to operation and maintenance (OM) are substantial. This paper describes a risk-based life cycle approach for optimal planning of OM. The approach is based on pre-posterior Bayesian decision theory, and can be used both to overall, initial planning of OM, and to sequential optimal decision making on planning of OM taking into account new information. Deterioration mechanisms such as fatigue, corrosion, wear and erosion are associated with significant uncertainty. Observations of the degree of damage can increase the reliability of predictions, especially in connection with condition-based maintenance. The approach can be used for gearboxes, generators, fatigue cracks, corrosion, etc. This paper also describes how probabilistic indicators can be used to quantify indirect information about the damage state for critical components, e.g. gearboxes.Operation and maintenance (OM) for offshore wind turbines contribute with a substantial part of the total life cycle costs, and can be expected to increase when wind farms are placed at deeper water depths and in harsher environments. This paper describes how a risk-based life cycle approach can be formulated for rational and optimal planning of operation (services, inspections, etc.) and maintenance (including repair and exchange) for offshore wind turbines. For other offshore installations such as oil and gas installations, cost-effective procedures for risk-based inspection planning have been developed during the last 10-15 years and are used at several locations worldwide, see e.g. Moan, 1 Faber et al. 2 and Sørensen and Faber. 3 These procedures are based on pre-posterior Bayesian decision theory, see e.g. Raiffa and Schlaifer 4 and Benjamin and Cornell. 5 This paper describes how procedures based on a similar theoretical basis can be applied for wind turbines, especially offshore wind farms. For wind turbines, the main aspects related to OM are availability, reliability and cost reductions.Maintenance activities can be divided in corrective and preventive (time-tabled or conditioned) maintenance. Conditioned maintenance using observations from, e.g. condition monitoring and inspections, should optimally be based on risk and pre-posterior Bayesian decision theory. This paper presents the basic principles and how they can be applied for wind energy to optimal planning of OM. Further, illustrative examples are given.

Wind turbines with a rated power of 5 to 6 MW are now being designed and installed, mostly for offshore operation. Within the EU supported UpWind research project, the barriers for a further increase of size, up to 20 MW, are considered. These wind turbines are expected to have a rotor diameter up to 250 m and a hub height of more than 150 m. Initially, the theoretical implications of upscaling to such sizes on the weight and loads of the wind turbines are examined, where it is shown that unfavourable increases in weight and load will have to be addressed. Following that, empirical models of the increase in weight cost and loads as a function of scale are derived, based on historical trends. These include the effects of both scale and technology advancements, resulting in more favourable scaling laws, indicating that technology breakthroughs are prerequisites for further upscaling in a cost-efficient way. Finally, a theoretical framework for optimal design of large wind turbines is developed. This is based on a life cycle cost approach, with the introduction of generic models for the costs, as functions of the design parameters and using basic upscaling laws adjusted for technology improvement effects. The optimal concept or concepts is obtained as the one that minimizes the total expected costs per megawatt hour (levelized production costs).