Power curves for wind turbines - Wind Power Generation and Wind Turbine Design - page 606

Figure 8 : Dynamical power characteristic of a multi-MW wind turbine. Wind
speed u n is normalized to equal power at standard conditions, and power
is normalized to rated power, both according to IEC 61400-12-1 [3]. For
a description of the measurements, see [11, 12].
of a multi-MW turbine [11, 12] which has been derived following the procedure
outlined above, including error bars. It can be seen that for most wind speeds the
power characteristic has very little uncertainty. Nevertheless, in the region where
rated power is approached larger uncertainties occur. Here it can be assumed that
the state of the power conversion is close to stability over a range of power values.
These larger uncertainties of the fi xed points can thus be interpreted as a conse-
quence of the changing control strategy of the wind turbine from partial to full
load range. It is of great interest how different turbines behave here, and may be
more or less power effi cient.
It is important to note that in [11] dynamical power characteristics have been
calculated using wind measurements taken by cup, ultrasonic, and LIDAR ane-
mometers. All three power characteristics were identical within measurement
uncertainty, showing that this approach appears quite robust concerning the wind
measurements.
2.4.2 Summary
The Langevin equation (8) clearly is a simplifying model of the complex power
conversion process. On the other hand, the drift function D (1) ( P ), see eqn (9), is
well defi ned for a large class of stochastic processes and not restricted to those
which obey the Langevin equation.
A central feature of the dynamical approach is the use of high frequency mea-
surement data as shown in Fig. 6, which enables the analysis of the short-time
dynamics of the power conversion process. The usage of the drift function eliminates
systematical errors caused by temporal averaging combined with the non-linear