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Figure 148: During simulation, FAST reads in wind speeds at the turbine directly from the
TurbSim output; the controller estimates blade positions 1/2 second into the future and
interpolates wind speeds at these positions from the annulus of evolved wind speeds. Signals
and blocks are explained throughout Sections 10.7.1 and 10.7.2.
10.7 Control Example 1: Wind Turbine Preview Control
In The Presence of Evolving Turbulence
In this control example, we use spectral methods discussedin Laks et al. (2013)so that a form
of wind evolution described in Section 10.5.3 can be introduced into the simulationof preview
control techniques. This is similar to the method proposed in Bossanyi et al. (2012b), but
in our case TurbSim’s von Karman spectral model (Jonkman, 2009) is generalized from the
originaltransverse andvertical (y,z)implementationto onethat encompasses(x,y,z) (directions
defined in Fig. 133). This introduces changes in the measured wind speeds that increase
with their displacement from the rotor, while keeping the spectral content and transverse and
vertical spatial correlations consistent with those used by TurbSim.
A schematic of the simulation proposed for investigating the effects of evolution is shown
in Fig. 148. A distribution of wind speeds is obtained from TurbSim (Jonkman, 2009) to
provide inputs during simulation of the turbine using FAST (Jonkman and Buhl, 2005).
These wind speeds are pre-processed to induce “evolved” wind speeds at locations within an
annulus located at various distances in front of the turbine as depicted in Fig. 133. During
simulation, preview measurements are taken from this annulus of evolved wind speeds. The
pre-processing and simulations are done three times; first simply generating an annulus of
wind speeds without evolution or lidar effects; then a second set with evolution effects; and
thenagainwiththeadditionofthegeometryandrangeweightingeffects ofacontinuous-wave
lidar measurement system.
The controller is designed to regulate speed and mitigate blade loads. The design is based
on a discrete-time equivalent of the turbine dynamics sampled at 20 Hz (0.05 s sample
period). With this approach, the preview measurements are stored in a chain of delays and
this storage can be viewed as a part of the state of a generalized turbine model. This allows
implementation of preview control as a state-feedback design; that is, the turbine control is
generated using feedback gains associated with each state of the generalized model and this
DTU Wind Energy-E-Report-0029(EN) 211