| 19 Nov 2025
Integrated Control of Floating Offshore Wind Farms with Reconfigurable Layouts
Abstract. This study proposes an integrated control method for floating offshore wind farms (FOWFs) that seeks to maximize farm-level power output or regulate it to a prescribed reference while mitigating wake-induced losses. To achieve these objectives, the method integrates existing control strategies: turbine repositioning, wake steering, power derating, and dynamic wake mixing, within a unified framework that adaptively selects the most effective combination based on wind conditions and control goals. This integration is motivated by the fact that individual strategies may be effective only under specific conditions or broadly effective but not always optimal, whereas their coordinated use can deliver robust performance improvements across a broad range of operating scenarios. The framework targets FOWFs with reconfigurable layouts, where turbines are mounted on floating platforms anchored to the seabed with sufficiently long and slack mooring lines, allowing them to shift within a certain range and thereby enabling controlled positional adjustments. Numerical simulations using the flow redirection and induction in steady state (FLORIS) engineering wake model show that the integrated method consistently outperforms any individual strategy. These findings highlight the potential of integrated control to enhance the efficiency, flexibility, and adaptability of FOWFs, offering a promising pathway to overcome the limitations and improve the performance of standalone control methods.
General comments:Â
The authors present a new wind farm control approach for offshore applications that combines turbines repositioning, wake steering, wake mixing, and power curtailment. They provide an introduction, a description of their methodology, and finally, the results of their study. The idea behind the work aligns well with current wind energy research on control, making it relevant.
At the same time, several important methodological details are currently missing or underdeveloped. Because the study relies heavily on FLORIS, the paper would benefit from a description of the chosen velocity, deflection, and turbulence models, along with the parameters used and the motivation for selecting them. These aspects have a strong influence on the results, and clarifying them would greatly strengthen the robustness and reproducibility of the findings.
Similarly, the implementation of wake mixing is not fully specified. For example, if the Helix model introduced recently in FLORIS is used, details such as the chosen amplitude and the rationale behind it would be valuable. The interaction between wake steering and curtailment could also be discussed more explicitly, especially given the complex aerodynamic behavior associated with simultaneous misalignment and changes in thrust coefficient.
Moreover, there is no mention of how curtailment is performed, which is rather crucial because it determines how the thrust coefficient changes.
It is also not clear whether the authors are combining wake mixing and wake steering simultaneously. Although this could be possible in FLORIS, there has not been a validation of the results yet. The effects on the wake do not simply sum up, and this would likely result in a critical overprediction of power.
Another point concerns the ambient conditions used in the simulations, such as turbulence intensity and vertical shear. These are key inputs to FLORIS and strongly influence wake recovery, so specifying them would improve transparency.
Overall, I believe that the results may not be reliable without a clear justification for the modeling choices.
To sum up, I believe the paper addresses an interesting and important topic. With a more complete description of the methodology and modeling assumptions, the results would become more reliable and easier for readers to interpret and reproduce.
If the authors are able to incorporate the suggested methodological clarifications, it could also be beneficial to expand the Results section. At present, the analysis feels somewhat limited, and given that the authors have developed a promising control framework, there is an opportunity to demonstrate its capabilities more extensively.
Specific comments:
- Line 52: the references for wake mixing could be updated to include more recent work.
- Line 56: a short discussion of how wake mixing can be implemented, with examples from the literature, would be helpful
- Line 59: a reference is needed to support the statement that the methods are cooperative rather than competitive
- Line 59: it would be good to clarify that wake mixing is not combined with wake steering in the cited work
- Line 109: the controller assumes knowledge of hub-height wind speed. Since this is difficult to measure in practice, adding a brief discussion would strengthen the paper
- Line 126: "Notably, power maximization can be regarded as a special case of pwoer regulation by srtting P_ref to the rated farm output", this sentence could be misleading, since the rated farm output typically refers to the greedy case, not to the power boosting case.
- Section 3: This section includes many repetitions of things that have been said already. It should be shortened in my opinion. For example:
  - Line 142: "A central challenge in wind farm operation is the presence of wake effects" this has been already said.
  - Lines 143–146, 161–163, 176–177, 184–186: These points appear to have been stated previously.
- Line 150: it is not clear what Beta is. In line 188, there is a brief mention that this is the blade pitch angle. This is then the Helix approach, but there is no mention, description, or reference to it.
- Line 181: reference missing: "Although less effective than repositioning (CIT) ... "
- Line 191: it seems like the authors are combining wake mixing with wake steering. This seems to push the capabilities of FLORIS a bit beyond its limits. As far as I know, there has not been any validation of this combination yet, and the effects do not really add up.
- Line 209: introducing weights in the cost function is sometimes inevitable, but it introduces important choices. How were these weights set and why? Was the cost function normalized somehow? The power tracking error and the velocity error have substantial differences in order of magnitude.
- Section 3.2.1: More detail is needed on how turbines are curtailed. The method strongly affects the thrust coefficient, so describing the approach is important
- Line 242: since a genetic algorithm is used with a large number of scenarios and variables, a short note on convergence criteria or computational considerations would be insightful
- Line 262: how was power maximization achieved with the cost function in Eq. 2? This seems to apply only to power regulation.
- Line 265: was a wind rose considered here? What turbulence intensity was sed?
- Figure 9: showing local inflow velocities would be informative, since they also appear in the cost function.
- Figure 9: the histograms refer to wake mixing, wake steering and integrated control. It would be interesting to see also the effect of repositioning alone.
- Line 306: the power regulation case was run at a wind speed of 18 m/s. Since this is above rated for most turbines, the controller behavior may be less interesting there. Running it in region II might give more insight. A brief explanation of the choice would help.
- Line 340: I encourage the authors to consider open-access sharing platforms, as this greatly supports reproducibility.
Technical comments:
- Line 126: reference missing: "... or ancillary service needs (CIT)."
- Figure 8 is difficult to read in black/white
- Figures 10a, 10b, 11a, 11b, 12a, 12b, 13a, 13b: axis labels should be added.
- In figures 10c, 11c, 12c, 13c, it is not entirely clear what the histograms represent; a short explanation in the caption would help.