the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Load Estimation in Onshore Wind Farms Using Surrogate Modeling and Generic Turbine Models
Abstract. This article investigates the development and application of surrogate models, based on slightly adapted generic turbine models, for predicting loads on real-world wind turbines. A small set of aeroelastic simulations provided training data for both Polynomial Chaos expansion and Gaussian Process regression models, which were trained to predict blade loads, tower accelerations, and their respective seed-to-seed variability. To evaluate the practical suitability of these models a case study was performed. Here, the surrogate models were applied to predict blade loads and tower accelerations respectively, using five years of SCADA data from an onshore wind farm. While the models approximated the real-world turbine behavior with a reasonable accuracy, the prediction quality varied across the different turbines in the park and was further influenced by factors such as the turbine's operational years and diurnal patterns suggesting a correlation with the turbulence intensity. Despite some limitations, the findings support the practicality of developing surrogate models for enabling efficient load estimations.
Competing interests: This research was conducted as part of Alexander Mönnig’s Master’s thesis in cooperation with Alterric Deutschland GmbH, where Alexander Mönnig was employed as a working student. Co-author Ansgar Hahn, an employee of Alterric Deutschland GmbH, supported the thesis as an industry supervisor. The company contributed by providing data access and domain expertise. The authors declare no other competing interests.
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 preprint. The responsibility to include appropriate place names lies with the authors.- Preprint
(8657 KB) - Metadata XML
- BibTeX
- EndNote
Status: open (until 03 Aug 2025)
-
RC1: 'Comment on wes-2025-112', Anonymous Referee #1, 22 Jul 2025
reply
The comment was uploaded in the form of a supplement: https://wes.copernicus.org/preprints/wes-2025-112/wes-2025-112-RC1-supplement.pdf
-
RC2: 'Comment on wes-2025-112', Anonymous Referee #2, 24 Jul 2025
reply
In this study, the authors developed and trained two surrogate models for predicting wind turbine loads and evaluated their performance using SCADA data. The surrogate models exhibited varying levels of accuracy in load prediction. The topic of using surrogate models for turbine performance evaluation is current and relevant to ongoing research in the field. The paper is generally well-structured, with a logical and coherent flow. However, it does not clearly emphasize its innovations, and the interpretation of the results is not fully convincing. The methodology, including the use of PCE and GPR models and reference wind turbine model, is well-established in the literature, and thus do not present novelty to the reader.
Furthermore, the interpretation of the prediction accuracy is questionable. For example, the annual median error in tower acceleration exceeds 40%, which raises concerns not only about the accuracy of the surrogate model itself, but also about the consistency between the reference turbine used in the OpenFAST simulation and the actual on-site load measurements. This concern hinders the reproduction, generalization, and practical application of the proposed approach (surrogate + generic WT models).
The attached comments are provided for each section of the manuscript, with the hope of improving the quality of the paper.
Data sets
Load Estimation in Onshore Wind Farms Using Surrogate Modelling and Generic Turbine Models Alexander Mönnig, Ulrich Römer https://doi.org/10.5281/zenodo.15380254
Model code and software
alexandermoennig/wind-farm-load-estimation: Wind Farm Load Estimation v1.0.0 Alexander Mönnig, Ulrich Römer https://doi.org/10.5281/zenodo.15446361
Viewed
HTML | XML | Total | BibTeX | EndNote | |
---|---|---|---|---|---|
126 | 20 | 11 | 157 | 6 | 7 |
- HTML: 126
- PDF: 20
- XML: 11
- Total: 157
- BibTeX: 6
- EndNote: 7
Viewed (geographical distribution)
Country | # | Views | % |
---|
Total: | 0 |
HTML: | 0 |
PDF: | 0 |
XML: | 0 |
- 1