Preprints
https://doi.org/10.5194/wes-2023-162
https://doi.org/10.5194/wes-2023-162
11 Jan 2024
 | 11 Jan 2024
Status: this preprint has been withdrawn by the authors.

Analysis of Turbine Yaw Misalignment Estimated by LIDAR Assuming Homogeneous Flow

Zhaoyu Zhang, Feng Guo, David Schlipf, Paolo Schito, and Alberto Zasso

Abstract. The 2D homogeneous flow assumption derived wind field reconstruction method is widely employed in the Doppler LIDAR. This paper aims to analyse the uncertainty in wind direction estimation and to improve the estimation accuracy. First, to quantify the uncertainty, a static model is proposed to describe the relationship between horizontal wind shear and yaw misalignment in LIDAR measurement. Subsequently, an analytic model of temporal-averaged misalignment uncertainty is built by using the Kaimal turbulence spectral model. This analytic model reveals that the standard deviation of yaw misalignment reaches approximately ±14° and ±12° for IEC turbulence class 'A' and 'B', respectively, regarding a temporal average over 60s. Obviously, these findings demonstrate that this LIDAR estimation method is insufficient to supervise the turbine yaw control system in terms of both accuracy and timeliness. Then, the temporal-averaged uncertainties obtained from the proposed analytic model are compared with simulations in various complexity, i.e. Kaimal, Mann spectral models, and Computational Fluid Dynamics, respectively. The rotor-average wind is set as the reference for measurement. Compared to an ideal sonic measurement, the LIDAR presents a worse estimation of rotor-effective wind direction estimation. These results show that increasing the fidelity of turbulence models does not alleviate the measurement uncertainty issue. Lastly, in an attempt to address the uncertainty issue, this study investigates the effects of adjusting the scanning pattern. The optimised parameters include measurement distance and horizontal half-open angle in a 2-beam LIDAR case and the additional vertical half-open angle in a 4-beam LIDAR case. However, even with the optimal scanning pattern, u and v wind components estimation could not acquire the same accuracy on the ideal sonic measurement simultaneously. Thereby, LIDAR can not guarantee wind speed and direction estimation quality simultaneously. In conclusion, this study highlights the yaw misalignment uncertainty in the LIDAR wind field reconstruction method based on 2D homogeneous flow assumption. The observed error levels remain consistent across varying fidelity turbulence models and scanning pattern adjustments. To address this challenge, future research should apply more advanced wind flow models to explore more accurate wind field reconstruction methods.

This preprint has been withdrawn.

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Zhaoyu Zhang, Feng Guo, David Schlipf, Paolo Schito, and Alberto Zasso

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on wes-2023-162', Jennifer Rinker, 27 Jan 2024
  • RC2: 'Comment on wes-2023-162', Anonymous Referee #2, 11 Feb 2024

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on wes-2023-162', Jennifer Rinker, 27 Jan 2024
  • RC2: 'Comment on wes-2023-162', Anonymous Referee #2, 11 Feb 2024
Zhaoyu Zhang, Feng Guo, David Schlipf, Paolo Schito, and Alberto Zasso
Zhaoyu Zhang, Feng Guo, David Schlipf, Paolo Schito, and Alberto Zasso

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Short summary
This paper aims to analyse the uncertainty in wind direction estimation of LIDAR and to improve the estimation accuracy. Findings demonstrate that this LIDAR estimation method is insufficient to supervise the turbine yaw control system in terms of both accuracy and timeliness. Future research should apply more advanced wind flow models to explore more accurate wind field reconstruction methods.
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