This study explores operations & maintenance requirements for offshore wind turbines. It does so by calculating performance, reliability and maintenance metrics from an operational database provided by a large offshore wind farm. Distributions of number of repairs and repair times per turbine are shared, as well as number of visits. A focus is placed on the effect of tidal access restrictions and position in the array by comparing clusters of turbines within the wind farm. It was found that tidal access restrictions lead to an increase in mean time to repair of 16%, and 0.22% decrease in technical availability. Turbines in the first few rows with reference to the prominent wind direction experience more minor failures on average, while those constantly operating in the wake of others are characterised by more major failures, and therefore a higher mean time to repair.
This article presents a Bayesian data‐modelling approach to assessing operational efficiency at offshore wind farms. Input data are provided by an operational database provided by a large offshore wind farm which employs an advanced data management system. We explore the combination of datasets making up the database, using them to train a Bayesian hierarchical model which predicts weekly lost production from corrective maintenance and time‐based availability. The approach is used to investigate the effect of technician work shift patterns, specifically addressing a strategy involving night shifts for corrective maintenance which was employed at the site throughout the winter. It was found that, for this particular site, there is an approximate annual increase in time‐based technical availability of 0.64%. We explore the effect of modelling assumptions on cost savings; specifically, we explore variations in failure rate, price of electricity, number of technicians working night shift, extra staff wages, months of the year employing 24/7 working and extra vessel provision. Results vary quite significantly among the scenarios investigated, exemplifying the need to consider the question on a farm‐by‐farm basis.
The penetration of ceftazidime into bone was determined by measuring the volume of distribution of 14C-ceftazidime in infected and non-infected bone of adult mongrel dogs. The volume of distribution in non-infected cortical bone was 0.114 +/- 0.011 l/kg (mean +/- S.E.M.) and increased significantly in non-infected immature callus to 0.484 +/- 0.13 l/kg (P less than 0.05). In the presence of infection, the volume of distribution in non-infected cortical bone was 0.144 +/- 0.05 l/kg, and significantly higher in infected reactive cortical bone, 0.453 +/- 0.07 (P less than 0.05). We conclude that ceftazidime penetrates infected and non-infected bone and that this penetration is greater into immature bone.
The extraction of ceftazidime has been measured in bone by means of the outflow dilution technique following injection into the perfused nutrient artery of the canine tibia. The instantaneous extraction was found to be 0.71 +/- 0.18 and the net extraction was 0.55 +/- 0.25 (mean +/- S.D. n = 5). The extraction of this antibiotic is relatively high and this experiment demonstrates the fact that antibiotics leave capillaries in bone and pass into the fluid spaces where they can act on pathogenic organisms.
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