Potential of Dynamic Wind Farm Control by Axial Induction in the Case of Wind Gusts
Abstract. Wind turbines organized in wind farms will be one of the main electric power sources of the future. Each wind turbine causes a wake with a reduced wind speed. This wake influences the power of downstream turbines. Therefore, there is a strong interaction between the individual wind turbines in a wind farm. This interaction is an opportunity for optimal control to maximize the total power and decrease the load (i.e., tower activity and pitch activity) of a wind farm. We use the already known axial-induction-based control but investigate its potential in the case of a wind gust using mathematical optimization. This case is particularly interesting because a wind gust requires a dynamic control reaction and the consideration of the time delay with which downstream turbines are affected. In particular, this enables to reduce the tower load of downstream wind turbines by dynamic axial-induction-based control of an upstream turbine.
Florian Bürgel et al.
Status: final response (author comments only)
- RC1: 'Comment on wes-2023-2', Anonymous Referee #1, 04 Mar 2023
- RC2: 'Comment on wes-2023-2', Anonymous Referee #2, 12 Mar 2023
Florian Bürgel et al.
Dataset for Article: Potential of Dynamic Wind Farm Control by Axial Induction in the Case of Wind Gusts https://doi.org/10.5281/zenodo.7520545
Florian Bürgel et al.
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The presented paper aims to explore the use of a dynamic wake model for wind turbine load mitigation during a gust event. The topic is interesting and relevant to the current research, as the dynamics between wind turbines have not been fully explored and exploited yet.
Unfortunately, the quality of the presented work is not fit for publication in my eyes: The presented manuscript has significant shortcomings in the areas methodology, linguistic quality and visual communication. All three are explained in further detail below. I hope that the authors are not discouraged by my comments and that they can serve to improve the presented work and future publications.
The main shortcoming of the methodology is the simulation tool: The authors choose to run their optimization in the WinFaST environment, a code which is rooted in the 2014 published FLORIDyn code by P. Gebraad. These kinds of parametric, dynamic models have the advantage of a fast computational time at the cost of fidelity. Recent publications have explored how turbine loads can accurately be modeled in similar, efficient frameworks , but this does have strong limitations. With this in mind, the model WinFaST is only cited as a footnote, and no validation study is linked, which would confirm WinFaST’s suitability to predict loads. This would have been fine if a validation study would have been conducted, with a proven tool, but this is not the case. State-of-the-art tools are not even mentioned in the text: Open-source tools such as FAST.Farm  are public to download, have the capability to run on a regular PC and have been thoroughly validated for load prediction. Another, similar tool is HAWC2Farm .
The model choice further becomes questionable when reading about its limitations to model wind gusts (page 14), the central element of the optimization challenge. The implementation of a wind field model, similar to the one employed in , could have possibly improved the setup.
The topic of wind gust modeling is also almost neglected in the manuscript. The presented work would have profited from a discussion around the topic and a motivation for the chosen options. The relevance of wind turbine design and the impact of gusts is a complex topic . And since the title of the presented work is hinting towards an investigation of the topic, it should be treated as such.
Many of the points above could have been deemed acceptable if the paper would have introduced a novel approach to the optimization problem and its solution. But the authors do not go to the lengths of understanding and exploiting the characteristics of their optimization problem and choosing an adequate optimizer. Instead, the optimizer choice discussion is reduced to a footnote and the application of a standard Matlab function.
In summary, the authors chose to research an interesting and complex research question but (i) fail to adequately motivate the choice of simulation tool and its legitimacy, (ii) they miss to express knowledge about the researched flow phenomena and justify the modeling of it and (iii) they do not conduct a thorough investigation of optimization algorithms and strategies.
As last remark, I would have liked to see a more in-depth review of the existing literature around wind turbine load mitigation and recent efforts, see  and more recently .
The linguistic quality of the paper is not fit for publication. The text reads as a loose collection of paragraphs, repeats itself frequently and is hard to follow. In my opinion, the paper needs to be rewritten in a much more concise way and without the use of colloquial language.
The presented figures are not polished and ready for publication. Flaws include, but are not exclusive to,
The authors also miss to communicate complex topics visually. One figure, which would have added a valuable overview is an input/output diagram of the optimalization setup.
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