An engineering model for 3-D turbulent wind inflow based on a limited set of random variables
Emerging stochastic analysis methods are of potentially great benefit for wind turbine power output and loads analysis. Instead of requiring multiple (e.g. 10 min) deterministic simulations, a stochastic approach can enable a quick assessment of a turbine's long-term performance (e.g. 20-yea...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2017-11-01
|
| Series: | Wind Energy Science |
| Online Access: | https://www.wind-energ-sci.net/2/507/2017/wes-2-507-2017.pdf |
| Summary: | Emerging stochastic analysis methods are of potentially great benefit for
wind turbine power output and loads analysis. Instead of requiring multiple
(e.g. 10 min) deterministic simulations, a stochastic approach can enable a
quick assessment of a turbine's long-term performance (e.g. 20-year fatigue
and extreme loads) from a single stochastic simulation. However, even though
the wind inflow is often described as a stochastic process, the common
spectral formulation requires a large number of random variables to be
considered. This is a major issue for stochastic methods, which suffer from
the <q>curse of dimensionality</q> leading to a steep performance drop with an
increasing number of random variables contained in the governing equations.
In this paper a novel engineering wind model is developed which reduces the
number of random variables by 4–5 orders of magnitude compared to typical
models while retaining proper spatial correlation of wind speed sample points
across a wind turbine rotor. The new model can then be used as input to
direct stochastic simulations models under development. A comparison of the
new method to results from the commercial code TurbSim and a custom
implementation of the standard spectral model shows that for a 3-D wind
field, the most important properties (cross-correlation, covariance, auto-
and cross-spectrum) are conserved adequately by the proposed reduced-order
method. |
|---|---|
| ISSN: | 2366-7443 2366-7451 |