No abstract

Abstract:The offshore wind industry is building and planning new wind farms further offshore due to increasing demand on sustainable energy production and already occupied prime resource locations closer to shore. Costs of operation and maintenance, transport and installation of offshore wind turbines already contribute significantly to the cost of produced electricity and will continue to increase, due to moving further offshore, if the current techniques of predicting offshore wind farm accessibility are to stay the same. The majority of offshore operations are carried out by specialized ships that must be hired for the duration of the operation. Therefore, offshore wind farm accessibility and costs of offshore activities are primarily driven by the expected number of operational hours offshore and waiting times for weather windows, suitable for offshore operations. Having more reliable weather window estimates would result in better wind farm accessibility predictions and, as a consequence, potentially reduce the cost of offshore wind energy. This paper presents an updated methodology of weather window prediction that uses physical offshore vessel and equipment responses to establish the expected probabilities of operation failure, which, in turn, can be compared to maximum allowable probability of failure to obtain weather windows suitable for operation. Two case studies were performed to evaluate the feasibility of the improved methodology, and the results indicated that it produced consistent and improved results. In fact, the updated methodology predicts 57% and 47% more operational hours during the test period when compared to standard alpha-factor and the original methodologies.

This paper briefly describes a novel approach of estimating weather windows for decision support in offshore wind turbine installation projects. The proposed methodology is based on statistical analysis of extreme physical responses of the installation equipment (such as lifting cable loads, motions of lifted objects, etc.), subjected to offshore met-ocean environment and limited by maximum allowable responses of the equipment used. An important aspect of any novel methodology is evaluating how well it performs compared to the standard methods given the same input. Hence, the main focus of this paper is on benchmarking the new methodology against the standard method for weather window estimationthe Alpha-factor method proposed by (DNV, 2011). The evaluation is done in a form of synthetic case study-an offshore wind turbine rotor lift operation at the FINO3 met-mast location. Performance of both methods is measured in terms of number and length of predicted weather windows.windows with higher accuracy would improve the estimates of transportation, installation and O&M costs of a wind farm and in turn could possibly reduce the LCOE of offshore wind energy.

This paper presents the application of a risk-and reliability-based inspection planning framework for the InnWind 20 MW reference wind turbine jacket substructure. A detailed fracture mechanics-based fatigue crack growth model is developed and used as a basis to derive optimal inspection plans for the jacket substructure. Inspection plans for different inspection techniques are proposed, and recommendations on how to optimize inspection intervals are discussed.

Monopiles are currently the most commonly used substructure in the offshore wind market due to their ease of installation in shallow to medium waters. The monopile and the transition piece are connected by a grouted joint. Fatigue and corrosion are two of the most important degradation mechanisms in this type of support structures. These mechanisms increase the costs and compromise the reliability of the structures. The development of new models and methodologies for the analysis of these degradation mechanisms is crucial. For this reason, a deterministic methodology to analyze the behavior of grouted joints has been developed considering the effect of corrosion on the steel parts of the grouted joints and the reliability of the joint under fatigue. Also, a probabilistic model for concrete grout fatigue strength and two probabilistic models for fatigue reliability assessment of steel components of the grouted connection have been developed.