Determining and understanding offshore wind turbine failure rates and resource requirement for repair are vital for modelling and reducing O&M costs and in turn reducing the cost of energy. While few offshore failure rates have been published in the past even less details on resource requirement for repair exist in the public domain. Based on ~350 offshore wind turbines throughout Europe this paper provides failure rates for the overall wind turbine and its sub-assemblies. It also provides failure rates by year of operation, cost category and failure modes for the components/sub-assemblies that are the highest contributor to the overall failure rate. Repair times, average repair costs and average number of technicians required for repair are also detailed in this paper. An onshore to offshore failure rate comparison is carried out for generators and converters based on this analysis and an analysis carried out in a past publication. The results of this paper will contribute to offshore wind O&M cost and resource modelling and aid in better decision making for O&M planners and managers

The offshore wind industry has historically focused on setting up new projects, with the decommissioning phase receiving little attention. This can cause future problems as decommissioning needs to be planned at the beginning to prevent complications that may arise, as it implies important operations and high costs. There are numerous features that make decommissioning a challenge, such as the marine environment, the technical limitations of vessels and the lack of specific regulations that determine what should be done, increasing the uncertainty of the process. Additionally, the unique characteristics of the sites involve exclusive optimal solutions for each project. This article analyses the main operation parameters that affect the decommissioning process, identifying the benefits and drawbacks of the influencing variables. A model is designed to compare different transportation strategies, searching for cost reduction. A decommissioning methodology is been proposed based on this analysis, taking into consideration the technical aspects of the process, and minimising environmental impacts. The model forecasts that the predicted duration and costs of this process are not being adequately captured in site decommissioning plans

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Condition monitoring (CM) systems are increasingly installed in wind turbines with the goal of providing component-specific information to wind farm operators, theoretically increasing equipment availability via maintenance and operating actions based on this information. In the offshore case, economic benefits of CM systems are often assumed to be substantial, as compared with experience of onshore systems. Quantifying this economic benefit is non-trivial, especially considering the general lack of utility experience with large offshore wind farms. A quantitative measure of these benefits is therefore of value to utilities and operations and maintenance (O & M) groups involved in planning and operating future offshore wind farms. The probabilistic models presented in this paper employ a variety of methods including discrete-time Markov Chains, Monte Carlo methods and time series modelling. The flexibility and insight provided by this framework captures the necessary operational nuances of this complex problem, thus enabling evaluation of wind turbine CM offshore. The paper concludes with a study of baseline CM benefit, sensitivity to O & M costs and finally effectiveness of the CM system itself.

Offshore wind turbine technology is moving forward as a cleaner alternative to the fossil fuelled power production. However, there are a number of challenges in offshore; wind turbines are subject to different loads that are not often experienced onshore and more importantly challenging wind and wave conditions limit the operability of the vessels needed to access offshore wind farms. As the power generation capacity improves constantly, advanced planning of Operation and Maintenance (O&M)activities, which supports the developers in achieving reduced downtime, optimised availability and maximised revenue, has gained vital importance. In this context, the focus of this research is the investigation of the most cost-effective approach to allocate O&M resources which may include helicopter, crew transfer vessels, offshore access vessels, and jack-up vessels. This target is achieved through the implementation of a time domain Monte-Carlo simulation approach which includes analysis of environmental conditions (wind speed, wave height, and wave period), operational analysis of transportation systems, investigation of failures (type and frequency), and simulation of repairs. The developed methodology highlights how the O&M fleets can be operated in a cost-effective manner in order to support associated day-to-day O&M activities and sustain continuous power production

Different configurations of gearbox, generator and power converter exist for offshore wind turbines. This paper investigated the performance of four prominent drive train configurations over a range of sites distinguished by their distance to shore. Failure rate data from onshore and offshore wind turbine populations was used where available or systematically estimated where no data was available. This was inputted along with repair resource requirements to an offshore accessibility and operation and maintenance model to calculate availability and operation and maintenance costs for a baseline wind farm consisting of 100 turbines. The results predicted that turbines with a permanent magnet generator and a fully rated power converter will have a higher availability and lower operation and maintenance costs than turbines with doubly fed induction generators. This held true for all sites in this analysis. It was also predicted that in turbines with a permanent magnet generator, the direct drive configuration has the highest availability and lowest operation and maintenance costs followed by the turbines with two-stage and three-stage gearboxes.

This paper investigates the relationship between wind turbine main‐bearing loads and the characteristics of the incident wind field in which the wind turbine is operating. For a 2‐MW wind turbine model, fully aeroelastic multibody simulations are performed in 3D turbulent wind fields across the wind turbine's operational envelope. Hub loads are extracted and then injected into simplified drivetrain models of three types of main‐bearing configuration. The main‐bearing reaction loads and load ratios from the simplified model are presented and analysed. Results indicate that there is a strong link between wind field characteristics and the loading experienced by the main bearing(s), with the different bearing configurations displaying very different loading behaviours. Main‐bearing failure rates determined from operational data for two drivetrain configurations are also presented.

The economic case for condition monitoring (CM) applied to wind turbines is currently not well quantified and the factors involved are not fully understood. In order to make more informed decisions regarding whether deployment of CM for wind turbines is economically justified, a refined set of probabilistic models capturing the processes involved are presented. Sensitivity of the model outputs with respect to variables of interest are investigated within the bounds of published data and expert opinion. The results show that the levels of benefit are dependent on a variety of factors including wind profile, typical downtime duration and wind turbine sub-component replacement cost