The Power Curve Working Group's assessment of wind turbine power performance prediction methods
<p>Wind turbine power production deviates from the reference power curve in real-world atmospheric conditions. Correctly predicting turbine power performance requires models to be validated for a wide range of wind turbines using inflow in different locations. The Share-3 exercise is the most...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2020-02-01
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| Series: | Wind Energy Science |
| Online Access: | https://www.wind-energ-sci.net/5/199/2020/wes-5-199-2020.pdf |
| Summary: | <p>Wind turbine power production deviates from the reference
power curve in real-world atmospheric conditions. Correctly predicting
turbine power performance requires models to be validated for a wide range
of wind turbines using inflow in different locations. The Share-3 exercise
is the most recent intelligence-sharing exercise of the Power Curve Working
Group, which aims to advance the modeling of turbine performance. The goal
of the exercise is to search for modeling methods that reduce error and
uncertainty in power prediction when wind shear and turbulence digress from
design conditions. Herein, we analyze data from 55 wind turbine power
performance tests from nine contributing organizations with statistical tests
to quantify the skills of the prediction-correction methods. We assess the
accuracy and precision of four proposed trial methods against the baseline
method, which uses the conventional definition of a power curve with wind
speed and air density at hub height. The trial methods reduce
power-production prediction errors compared to the baseline method at high
wind speeds, which contribute heavily to power production; however, the
trial methods fail to significantly reduce prediction uncertainty in most
meteorological conditions. For the meteorological conditions when a wind
turbine produces less than the power its reference power curve suggests,
using power deviation matrices leads to more accurate power prediction. We
also determine that for more than half of the submissions, the data set has a
large influence on the effectiveness of a trial method. Overall, this work
affirms the value of data-sharing efforts in advancing power curve modeling
and establishes the groundwork for future collaborations.</p> |
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| ISSN: | 2366-7443 2366-7451 |