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Figure C1: Histogram of NMEs using the Baseline method: (a) NMEs of Inner Range (dark blue) and Outer Range (light blue) before filtering; (b) Inner Range NMEs after filtering out the submissions with absolute NMEs larger than 1%, categorized into three Inner Range definitions-definition A in dark green, definition B in green, and definition C in lime; (c) Outer Range NMEs categorized into three Inner Range definitions with the same color scheme as in (b).
Source publication
Abstract. 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...
Contexts in source publication
Context 1
... implications. For instance, the number of the 10-minute data sample in the Outer Range is larger than that in the Inner Range for all of the submissions (Fig. 5a). In three submissions, the sample size of the 10-minute Outer Range data is more than seven times than that of the Inner Range (Fig. 5b). Note that the NME filter (Appendix C1 and Fig. C1) is applied to remove erroneous submissions from all the results presented for rest of the manuscript. 300 ...Context 2
... each submission should record an Inner Range NME of zero. In other words, by definition the turbine should produce at or above capacity on average in the Inner Range. Hence, we exclude a total of three erroneous submissions with large, nonzero NMEs in the Inner Range (nonzero blue bars on the left in Fig. C1a). Note that all of the three submissions are 735 from the same ...Context 3
... filtering, the Inner Range NMEs hover around 0% (Fig. C1b); the Outer Range NMEs span almost 15% around 0% (Fig. C1c). In this manuscript, we only evaluate the 52 Inner Range NME-filtered submissions in Sect. 4, unless stated otherwise. As stated in Sect. 3.4, we introduce a fifth bin of Inner Range TI and Outer Range wind shear (ITI-OS) for those ...Context 4
... filtering, the Inner Range NMEs hover around 0% (Fig. C1b); the Outer Range NMEs span almost 15% around 0% (Fig. C1c). In this manuscript, we only evaluate the 52 Inner Range NME-filtered submissions in Sect. 4, unless stated otherwise. As stated in Sect. 3.4, we introduce a fifth bin of Inner Range TI and Outer Range wind shear (ITI-OS) for those ...Similar publications
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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 Wor...
The growing size of wind turbines leads to extremely high tip speeds when the blades are rotating. The blades are prone to leading edge erosion when raindrops hit the blades at such high speeds and blade damage will eventually affect the power production until repair or replacement of the blade is performed. Since these actions come with a high cos...
Citations
The power generation of wind turbines varies depending on external environmental conditions. To present universal correlations between conditions that affect wind speed and wind turbine power, this study analyzed the effects of three atmospheric factors—atmospheric stability, turbulence intensity (TI), and wind shear exponent (WSE)—on the power performance and annual energy production (AEP) of wind turbines. Additionally, the horizontal and vertical speed components of the 3D sonic anemometer were investigated. At two measurement heights, unstable regimes showed an ascending air current, whereas stable regimes simultaneously showed both ascending and descending currents. Power curves calculated for each atmospheric factor regime revealed that atmospheric stability (200 kW–11 m/s) exhibited the greatest difference, followed by TI (91 kW–11 m/s) and WSE (32 kW–10.5 m/s). Finally, AEP was calculated at the annual mean wind speed of the study site and exhibited variations of 1.4–4% according to the factor regime. The AEP difference due to the change in atmospheric stability was greatest, and AEP was highest in moderately unstable atmospheric conditions. Clearer understanding of the effects of atmospheric factors on the power characteristics and AEP of wind turbines is expected to deliver practical benefits for wind-resource assessment and power-production prediction.
