Environmental Engineering Reference
In-Depth Information
In the fast region, the transition happens between the partial load and the full load
modes. The dynamics of these transitions can be observed in Fig. 10. In both regions,
the two different modes of operation are clearly separated by the Langevin power
curve, while the IEC power curve averages both modes into an intermediate value.
The two different wind turbines represented in Figs 9 and 10 were very well
characterized by the Langevin power curve. The method applies to all (horizontal-
axis) wind turbines, even when presenting complex dynamics. Wind turbines
equipped with multiple gears were characterized successfully using this method,
when the IEC method revealed its limitations. The Langevin power curve is a
powerful tool to visualize and quantify power performance.
3.2 Monitoring wind turbines
Monitoring is closely related to the idea of characterization (introduced in the
previous section), as it follows the evolution in time of the power characteristics.
Once a machine was characterized using the dynamical approach, it becomes pos-
sible to compare and monitor power performance on a regular basis. Dynamical
anomalies can be rapidly brought to light when deviations appear on two consecu-
tive Langevin power curves. The precision of the method allows localizing the
anomaly in the domain {
u
;
P
}, giving more insight towards the defi cient com-
ponent of the wind turbine. Applied on a monthly (or even weekly) basis, such
monitoring can prevent anomalies from limiting the power production, or worse,
damaging other components of the wind turbine.
3.3 Power modeling and prediction
Once a machine was characterized using the dynamical approach, basically once
the drift fi eld
D
(1)
(
P
;
u
) and the diffusion coeffi cient
D
(2)
(
P
;
u
) were computed, it
becomes possible to model the power output
P
(
t
) from any input wind speed time
series
u
(
t
). The Langevin equation [see eqn (8)] can be solved knowing
D
(1)
(
P
;
u
)
and
D
(2)
(
P
;
u
) to generate a realistic power output
P
(
t
).
A simple, artifi cial case was created in Fig. 11. In this fi gure, one can see the
real power output
P
(
t
) of a wind turbine, then the same quantity modeled in good
running condition, and fi nally modeled with an artifi cial anomaly (that limits the
power extraction to roughly 45% of the rated power
P
r
). A comparison of the fi rst
and second graph shows that through a simple model, it is possible to estimate the
power output
P
(
t
) of a wind turbine knowing only
D
(1)
(
P
;
u
) and
D
(2)
(
P
;
u
). This
model can be applied to any wind situation
u
(
t
), as an effective way to study the
behavior of a wind turbine in different wind conditions.
This model shows great potential in the continuing evolution of current methods,
principally in the prediction of power production. When coupled with a meteoro-
logical wind forecast, the model could be used to generate the power output of a
wind turbine (whose power performance has been characterized). In addition to
providing quantitative power production estimates, power quality, i.e. fl uctuations
in power, stability, and regularity of the high frequency power output
P
(
t
) too will
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