Wind Gust Detection and Impact Prediction for Wind Turbines

Abstract

Wind gusts on a scale from 100 m to 1000 m are studied due to their significant influence on wind turbine performance. A detecting and tracking algorithm is proposed to extract gusts from a wind field and track their movement. The algorithm utilizes the "peak over threshold method," Moore-Neighbor tracing algorithm, and Taylor's frozen turbulence hypothesis. The algorithm was implemented for a three-hour, two-dimensional wind field retrieved from the measurements of a coherent Doppler lidar. The Gaussian shape distribution of the gust spanwise deviation from the streamline was demonstrated. Size dependency of gust deviations is discussed, and an empirical power function is derived. A prediction model estimating the impact of gusts with respect to arrival time and the probability of arrival locations is introduced, in which the Gaussian plume model and random walk theory including size dependency are applied. The prediction model was tested and the results reveal that the prediction model can represent the spanwise deviation of the gusts and capture the effect of gust size. The prediction model was applied to a virtual wind turbine array, and estimates are given for which wind turbines would be impacted.

Comments on “Wind Gust Detection and Impact Prediction for Wind Turbines”

“…We thank Zhou et al [1] for citing these two papers [5,6]. Their statements regarding our research results are however inaccurate and do not include the latest peer-reviewed research in the field.…”

“…While their paper mentions cross-correlation (CC) and wavelet-based optical flow (WOF) algorithms as alternative ways to obtain such wind fields, statements about these two methods are inaccurate and do not consider the latest published research on this subject. In this note, we correct statements in [1] based on our recent work [3,4] and put the CC, OF, and 2D-VAR methods into perspective. We contend that the WOF and 2D-VAR algorithms are in fact similar and that motion estimation methods should not be discounted for remote wind sensing applications such as the one they developed.…”

“…Zhou et al [1] described an algorithm to detect and track wind gusts. Their algorithm operates on two-dimensional (2D) two-component (2C) wind fields which must first be made available.…”

“…Alternative approaches to remotely observe similar 2D-2C wind fields are also mentioned in the introduction of the paper: "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).…”

“…Zhou et al [1] provided an excellent example of the value of remote observations of two-component wind fields in the lower atmospheric boundary layer. They obtained two-component wind fields by applying a 2D variational (2D-VAR) method [2] to single component velocity data from a single Doppler lidar.…”