Abstract
Considering loads when optimizing wind-farm layouts or designing farm-control strategies is important, but the computational cost of using high-fidelity wake models in the loop can be prohibitively high. Using simpler models that consider only the spatial variation of turbulence statistics is a tempting alternative, but the accuracy of these models with respect to the aeroelastic response is not well understood. This paper therefore highlights the effect of replacing wake meandering with spatially varying statistics (“profile functions”) in the inflow to a downstream turbine. Profile functions at different downstream and lateral locations are extracted from a large-eddy simulation with an upstream turbine and compared with two lower-fidelity models: one that prescribes both the mean and standard deviation of the turbulence and one that prescribes only the mean. The aeroelastic response of an NREL 5 MW wind turbine is simulated with the three different wake-model fidelities, and various quantities of interest are compared. The mean values for the power and rotor speed for the medium-and low-fidelity model match well, but the accuracy of the fatigue loads varies greatly depending on the load channel. Prescribing the profile function for the standard deviation is only beneficial for the tower-base fore-aft moment; all other DELs had similar accuracies for both the medium- and low-fidelity models. The paper concludes that blade DELs can be estimated using these simple models with some accuracy, but care should be taken with the load channels related to the shaft torsion and tower-base fore-aft bending moment.
Highlights
The main driver in the development of wind turbine technology is the reduction in the cost of wind energy, whether measured in terms of the levelized cost of energy (LCOE), internal rate of return (IRR) or some other economic metric [1]
Using time-series constraints is more accurate than profile functions and can reproduce interesting transient phenomena, this paper focuses on steadystate wake models and utilizes profile functions on the mean wind speed and turbulence standard deviation
It is of interest to quantify the variability present in the medium- and low-fidelity turbulence boxes caused by the choice of the random seed
Summary
The main driver in the development of wind turbine technology is the reduction in the cost of wind energy, whether measured in terms of the levelized cost of energy (LCOE), internal rate of return (IRR) or some other economic metric [1]. As more and more turbines are placed in closely clustered wind farms, this emphasis on cost reduction has expanded from a single-turbine view to a farm-level view. For wind farms already erected and operating, reductions in cost could be achieved via recent developments in farm-level control. Many recent publications that have investigated wind farm control have concluded that various techniques can significantly increase wind farm production [2,3,4], and industry has begun to implement farm-level control techniques such as wake steering in their installations [5]. For wind plants that are currently being designed, novel layout optimization techniques provide further opportunities to increase plant production [6,7].
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