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We present here a computational fluid dynamics (CFD) simulation of Lillgrund offshore wind farm, which is located in the Øresund Strait between Sweden and Denmark. The simulation combines a dynamic representation of wind turbines embedded within a Large-Eddy Simulation CFD solver, and uses hr-adaptive meshing to increase or decrease mesh resolution where required. This allows the resolution of both large scale flow structures around the wind farm, and the local flow conditions at individual turbines; consequently, the response of each turbine to local conditions can be modelled, as well as the resulting evolution of the turbine wakes. This paper provides a detailed description of the turbine model which simulates the interaction between the wind, the turbine rotors, and the turbine generators by calculating the forces on the rotor, the body forces on the air, and instantaneous power output. This model was used to investigate a selection of key wind speeds and directions, investigating cases where a row of turbines would be fully aligned with the wind or at specific angles to the wind. Results shown here include presentations of the spin-up of turbines, the observation of eddies moving through the turbine array, meandering turbine wakes, and an extensive wind farm wake several kilometres in length. The key measurement available for cross-validation with operational wind farm data is the power output from the individual turbines, where the effect of unsteady turbine wakes on the performance of downstream turbines was a main point of interest. The results from the simulations were compared to performance measurements from the real wind farm to provide a firm quantitative validation of this methodology. Having achieved good agreement between the model results and actual wind farm measurements, the potential of the methodology to provide a tool for further investigations of engineering and atmospheric science problems is outlined.

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The use of offshore wind farms has been growing in recent years. Europe is presenting a geometrically growing interest in exploring and investing in such offshore power plants as the continent's water sites offer impressive wind conditions. Moreover, as human activities tend to complicate the construction of land wind farms, offshore locations, which can be found more easily near densely populated areas, can be seen as an attractive choice. However, the cost of an offshore wind farm is relatively high, and therefore, their reliability is crucial if they ever need to be fully integrated into the energy arena. This paper presents an analysis of supervisory control and data acquisition (SCADA) extracts from the Lillgrund offshore wind farm for the purposes of monitoring. An advanced and robust machine-learning approach is applied, in order to produce individual and population-based power curves and then predict measurements of the power produced from each wind turbine (WT) from the measurements of the other WTs in the farm. Control charts with robust thresholds calculated from extreme value statistics are successfully applied for the monitoring of the turbines.Index Terms-Machine learning, offshore wind farm, pattern recognition, supervisory control and data acquisition (SCADA), wind turbine (WT) monitoring.This work is licensed under a Creative Commons Attribution 3.0 License. For more information, see http://creativecommons.org/licenses/by/3.0/