Articles | Volume 11, issue 7
https://doi.org/10.5194/wes-11-2405-2026
© Author(s) 2026. This work is distributed under the Creative Commons Attribution 4.0 License.
Wind speed estimation using second-order sliding-mode observers: simulation and experimental validation on a floating offshore wind turbine
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- Final revised paper (published on 08 Jul 2026)
- Preprint (discussion started on 21 Nov 2025)
Interactive discussion
Status: closed
Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor
| : Report abuse
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RC1: 'Comment on wes-2025-206', Anonymous Referee #1, 03 Dec 2025
- AC1: 'Reply on RC1', Moein Sarbandi, 16 Jan 2026
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RC2: 'Comment on wes-2025-206', Anonymous Referee #2, 09 Dec 2025
- AC2: 'Reply on RC2', Moein Sarbandi, 16 Jan 2026
Peer review completion
AR – Author's response | RR – Referee report | ED – Editor decision | EF – Editorial file upload
AR by Moein Sarbandi on behalf of the Authors (16 Jan 2026)
Author's response
Author's tracked changes
Manuscript
ED: Referee Nomination & Report Request started (10 Feb 2026) by Shawn Sheng
RR by Anonymous Referee #1 (13 Mar 2026)
ED: Publish as is (07 May 2026) by Shawn Sheng
ED: Publish as is (28 May 2026) by Paul Fleming (Chief editor)
AR by Moein Sarbandi on behalf of the Authors (31 May 2026)
Manuscript
Review of Preprint wes-2025-206.
The paper proposes a novel second order sliding mode observer (SOSMO) for wind estimation on floating off shore wind turbines. Two methods are introduced and compared with the extended Kalman filter (CD-EKF) available in ROSCO. Simulation studies with FAST and Turbsim show that the SOSMO observers can produce wind estimates with smaller RMS error than those from CD-EKF. The results are further validated on a FOWT in laboratory-scale software in the loop experiments. Three wind profiles are considered and in each case the SOSMO observers again achieve smaller RMS errors than CD-EKF.
The paper is clearly written and the motivation is clear. Some comments:
The reference for Lidar (Jena and Rajendran, 2015) is now rather dated. The use of Lidar for WTC has been extensively investigated and was the subject of the IEA Wind Task 32. The authors are recommended to view their publications on the utility of Lidar for turbine control, available from https://zenodo.org/communities/ieawindtask32/about and use these to provide a more contemporary assessment on the utility of Lidar for WTC.
The discussion of computation time is too brief, what does runtime refer to? The computation for the convergence time for each algorithm needs to be shown, to enable comparisons of their suitability for real-time control implementation.
In the SIL experiments, the wind speed error is v - \hat v, but I didn’t see an explanation of how the actual wind speed v was obtained.
The use of rotor speed to estimate wind speed has been investigated for quite some time, see for example the survey in Soltani, et al Estimation of Rotor Effective Wind Speed: A Comparison, TCST 2013 https://doi.org/10.1109/TCST.2013.2260751 Due to their highly stochastic nature, all wind estimates requires low pass filtering before they can be used for control purposes. Indeed, it is clear from Figure 11-13 that all three estimation methods filter the wind. The important question is how well they preserve the portion of the spectrum that is useful for control, and RMS error may not be a good measure for this.
Ultimately, the real test of a wind speed estimate is its utility for WTC, and this aspect of REWs has been extensively investigated in several recent papers, notably Guo, F. et al Evaluation of lidar-assisted wind turbine control under various turbulence characteristics, WES 2023 https://doi.org/10.5194/wes-8-149-2023 and the cited WES paper by Moldenhauer 2025. These papers discussed many features of wind estimation and filtering for their implementation within control methodologies. The wind estimation methods were then combined with novel control methodologies to deliver improved WTC control for turbine fatigue load reduction.
Overall, the study is interesting and well presented, but the contribution is rather limited in scope and insufficient for a strong journal like WES. In its present form, it would be well suited to a conference presentation like WESC or Torque. If the authors wish to extend their work, they may consider combining SOSMO wind estimation with WTC methods (possibly combining it with Lidar) and demonstrating improvements in some aspects of turbine control performance.