the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 23 Sep 2026
Brief communication: Enhanced wind turbine fatigue load estimation using digital shadows with data-driven bias correction
Abstract. Building on a previously published digital-shadow framework for wind turbine load estimation, this study augments a linearized aeroelastic model with learning-based bias correction (BC). A neural network (NN) learns operating-condition-dependent corrections while preserving the physics-based model structure. The framework is validated using field measurements under simple, complex, and mixed inflow conditions. For blade bending moments at the 25% span location, damage equivalent load (DEL) errors of 14-24% without correction are reduced to below 5%. The largest improvements occur under complex inflow. A hybrid strategy combines baseline and NN-based corrections, improving robustness across varying operating and inflow conditions.
Competing interests: At least one of the (co-)authors is a member of the editorial board of Wind Energy Science.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.- Preprint
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Status: open (until 21 Oct 2026)
- CC1: 'Comment on wes-2026-156', J. Gordon Leishman, 26 Sep 2026 reply
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CC2: 'Comment on wes-2026-156', J. Gordon Leishman, 26 Sep 2026
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I also note that Carlo L. Bottasso is the corresponding author of this manuscript and, more importantly, the Editor-in-Chief of Wind Energy Science. The manuscript discloses only that at least one author is a member of the journal's editorial board. Given the corresponding author's position as Editor-in-Chief, this represents a significant potential conflict of interest in the paper's editorial handling. In my view, an independent editor should handle the manuscript, reviewer selection, review evaluation, and any publication decision, with no involvement from the corresponding author in any editorial capacity. For transparency, the public review record should also clarify the identity and independence of the handling editor.
Disclaimer: this community comment is written by an individual and does not necessarily reflect the opinion of their employer.Citation: https://doi.org/10.5194/wes-2026-156-CC2 -
RC1: 'Comment on wes-2026-156', Anonymous Referee #1, 07 Oct 2026
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Please see attachment.
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RC2: 'Comment on wes-2026-156', Anonymous Referee #2, 13 Oct 2026
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Although the proposed learning-based bias correction is potentially useful, I do not consider the dataset and training-validation framework sufficient to establish that it reliably improves fatigue-load estimation across varying inflow conditions.
The training data comprise 43 h of simple inflow and 20 h of complex inflow from a single turbine at one site, collected during two campaigns in the same month. The corresponding test sets contain only 6 h and 3 h. High-frequency sampling does not necessarily provide a large number of independent training examples because of temporal correlation. The manuscript does not adequately establish whether these data cover sufficient independent operating conditions for training or how temporal separation between training and testing was ensured. The additional 7.5 h of mixed and transitional inflow provide useful within-turbine holdout validation, but offer limited evidence of performance beyond the experimental setting used for training.
The hybrid results also require clarification. Under complex inflow, Table 1 reports a DEL error of 13% for the hybrid strategy, identical to the baseline correction, whereas the harmonic-based NN achieves 4%. Thus, the hybrid provides no improvement over the baseline in this case despite the better performance of the harmonic-based NN. The manuscript should explain why the switching strategy does not capture this improvement. Although this result alone does not disprove robustness, it leaves the claimed benefit insufficiently demonstrated.
A single-turbine study can provide a valuable proof of concept. However, the present manuscript does not sufficiently establish the adequacy of its training data or the independence and reliability of its validation. Addressing these concerns requires stronger experimental evidence and a clearly documented, temporally separated evaluation, rather than presentation changes alone.
Citation: https://doi.org/10.5194/wes-2026-156-RC2
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I reviewed the previous version of this work. The present manuscript is considerably shorter and more focused, and several aspects of the presentation have been clarified. However, I do not find that the principal scientific concern raised previously has been materially resolved. The underlying experimental evidence remains essentially the same, and in my view the manuscript has largely been repackaged and shortened rather than substantially strengthened.
My main concern is the very limited dataset used to establish the performance and generality of the proposed approach. The evaluation is based on measurements from a single 3.5 MW eno126 turbine at one site, with data obtained during two measurement campaigns in October 2020. After filtering, approximately 49 h of simple-inflow data and 23 h of complex-inflow data remain, of which only 6 h and 3 h, respectively, are reserved for testing. An additional 7.5 h of mixed and transitional inflow data are used to examine generalization.
These data may be sufficient to show that the proposed method performs satisfactorily for this particular turbine, site, instrumentation, and measurement period. They are not, in my opinion, sufficient to establish the broader generality of a data-driven bias-correction methodology. The so-called unseen datasets are still obtained from the same turbine, at the same site, using the same instrumentation, and during the same overall measurement campaign. This constitutes holdout validation within a single experimental dataset rather than genuinely independent external validation.
This issue is particularly important for a learning-based correction method. The learned relationship may depend on turbine-specific structural properties, controller behavior, sensor characteristics, atmospheric conditions, and site characteristics. The authors present no evidence that the correction transfers to another turbine, another measurement campaign, another season, or another site. The authors identify wake flow, yawed conditions, offshore conditions, additional turbine platforms, and strongly transient operation as subjects for future work. These are precisely the kinds of conditions needed to establish the method's broader applicability.
A secondary concern is the degree of independence of the load validation. The manuscript states that the digital shadow assimilates blade-root bending moments and provides the training signal for the correction, whereas the blade bending moment at the 25% span location is subsequently used as a validation quantity. A bending moment is an integrated structural response to aerodynamic, inertial, gravitational, and dynamic loading. It is not a unique measure of the underlying aerodynamic load distribution. Furthermore, root and inboard blade bending moments are structurally related quantities. Agreement with the bending moment at another spanwise station is therefore useful as a check on structural-load estimation, but it does not constitute a strongly independent demonstration that the underlying aerodynamic loading has been reconstructed correctly.
The revised manuscript does provide a clearer comparison between the uncorrected digital shadow, the baseline bias correction, the neural-network approaches, and the hybrid method. This is useful. However, these comparisons do not address the more fundamental limitation that all results arise from a relatively restricted experimental dataset. I remain unconvinced that the evidence presented supports the generality the manuscript implies. The work demonstrates the method's performance for the particular turbine and datasets considered, but it has not established broader applicability. In my view, shortening and reorganizing the previous manuscript has not resolved this central limitation.