Comments on “Wind Gust Detection and Impact Prediction for Wind Turbines”
Abstract
:1. Introduction
2. Statements in Zhou et al. [1] About Motion Estimation Methods
“Mayor adapted two computer-vision methods for flow motion estimation: the cross-correlation method and the wavelet-based optical flow method [5,6].”—(Zhou et al. [1], Section 1).
“[...] the cross-correlation method has limitations for non-uniform velocity fields and the optical flow method requires relatively small (few pixels) movement and is computationally demanding. These requirements make them impractical.”—(Zhou et al. [1], Section 1).
3. The Practicality of Motion Estimation Methods
4. Comparing CC, WOF, and 2D-VAR Methods
5. Conclusions
Author Contributions
Conflicts of Interest
References
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Method | Hamada et al. [4] (Cross-Correlations) | Dérian et al. [3] (Wavelet-Based Optical Flow) | Zhou et al. [1] (2D-VAR) |
---|---|---|---|
Domain shape | sector, 0.5 km to 3–5 km range | sector, 0.5 km to 3–5 km range | 6 km × 4 km |
Domain area | 4.6 km to 13 km | 4.6 km to 13 km | 24 km |
Vector spacing | sparse | dense, 8 m | dense, 80 m |
Number of vectors | variable | ≈ to | ≈ |
Time step | ≈ s | ≈ s | 30 s |
Real-time computations | yes | yes | “possible” |
Method | Hamada et al. [4] (Cross-Correlations) | Dérian et al. [3] (Wavelet-Based Optical Flow) | Cherukuru et al. [2] (2D-Var) |
---|---|---|---|
Reference measure | Streamline Doppler lidar | Streamline Doppler lidar | cup and vane anemometer |
Number of points (duration) | 891 (≈150 h) | 892 (≈150 h) | 120 (20 h) |
Wind speed RMS error | n.r. | n.r. | 0.383 m·s−1 |
Wind speed correl. coeff. | n.r. | 0.96 | |
Wind direction error | n.r. | ||
Wind direction correl. coeff. | n.r. | 0.98 | |
West–east component u RMS error | 0.36 m·s−1 | 0.29 m·s−1 | n.r. |
West–east component u correl. coeff. | n.r. | ||
South–north component v RMS error | 0.37 m·s−1 | 0.29 m·s−1 | n.r. |
South–north component v correl. coeff. | n.r. |
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Mayor, S.D.; Dérian, P. Comments on “Wind Gust Detection and Impact Prediction for Wind Turbines”. Remote Sens. 2018, 10, 1625. https://doi.org/10.3390/rs10101625
Mayor SD, Dérian P. Comments on “Wind Gust Detection and Impact Prediction for Wind Turbines”. Remote Sensing. 2018; 10(10):1625. https://doi.org/10.3390/rs10101625
Chicago/Turabian StyleMayor, Shane D., and Pierre Dérian. 2018. "Comments on “Wind Gust Detection and Impact Prediction for Wind Turbines”" Remote Sensing 10, no. 10: 1625. https://doi.org/10.3390/rs10101625