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Comment of
Remote Sens. 2018, 10(4), 514.
Comments on “Wind Gust Detection and Impact Prediction for Wind Turbines”
by
Shane D. Mayor 1,*
and Pierre Dérian 2
and Pierre Dérian 2
1
California State University Chico, 400 West First Street, Chico, CA 95929, USA
2
Independent researcher, 44000 Nantes, France
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(10), 1625; https://doi.org/10.3390/rs10101625
Received: 12 July 2018 / Revised: 8 October 2018 / Accepted: 10 October 2018 / Published: 12 October 2018
(This article belongs to the Special Issue Remote Sensing of Atmospheric Conditions for Wind Energy Applications)
We refute statements in “Zhou, K., et al. Wind gust detection and impact prediction for wind turbines. Remote Sens. 2018, 10, 514.” about the impracticality of motion estimation methods to derive two-component vector wind fields from single scanning aerosol lidar data. Our assertion is supported by recently published results on the performance of two image-based motion estimation methods: cross-correlation (CC) and wavelet-based optical flow (WOF). The characteristics and performances of CC and WOF are compared with those of a two-dimensional variational (2D-VAR) method that was applied to radial velocity fields from a single scanning Doppler lidar. The algorithmic aspects of WOF and 2D-VAR are reviewed and we conclude that these two approaches are in fact similar and practical.
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Keywords:
lidar; wind; Doppler; aerosol; motion estimation; optical flow; cross-correlation; wind energy; gust prediction; variational analysis
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
MDPI and ACS Style
Mayor, S.D.; Dérian, P. Comments on “Wind Gust Detection and Impact Prediction for Wind Turbines”. Remote Sens. 2018, 10, 1625.
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