Presentation Description: Recent advance in turbulence enable time series enable obtaining cross-distribution of different variables (wind speed, shear, intensity of turbulence, veers, air density) to compute number hours per year that wind turbine will fall inside/outside the ‘certified’ power curves range (inner and outer range respectively - see Power Curve Working Group, pcwg.org, for more details on this issue).
Outer range has a significant control on project AEP estimations bias. Moreover, the outer range performance uncertainty is amplified by the fast evolving rotor size and aerodynamics complexity which requires multi-dimensional power curve approaches. We can expect the power curve ranges to become more dependent on the specific conditions at each wind turbine position.
A key limitation of the current methods for considering outer range conditions is that they assume that the wind conditions (e.g. turbulence) at the met mast apply to all turbine locations.
This work analysis an example use case of mapping inner/outer range across different positions for a real windfarm project using WRF LES as predictor of turbine location wind conditions. Operational turbine performance data provided by Pattern Energy will be employed to supplement the WRF LES predictions. The work demonstrates the use of time dependent full physics flow modeling to characterise the uncertainty of turbine power performance by flagging the outer range in time and in space.
Methodology: The presentation will use effective visualisation of the results including animation of the model time series.