the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 08 Jan 2025
Dynamic induction control for mitigation of wake-induced power losses: a wind tunnel study under different inflow conditions
Abstract. Dynamic induction control (DIC), also known as the pulse method, is a wake mixing strategy that has shown promising results for mitigating wake-induced power losses in wind farms. It relies on dynamic collective blade pitching to enhance turbulent mixing, thereby accelerating the wake recovery. Experimental validation of this concept has been primarily limited to single-turbine cases under idealised conditions without shear and negligible turbulence. This paper presents a wind tunnel study to investigate the wake recovery improvement induced by DIC in single- and two-turbine configurations, as well as potential power gains in a three-turbine array. The study includes experiments under baseline uniform inflow and two realistic atmospheric boundary layer inflows. Short-range continuous-wave lidar measurements are used to remotely map the time-averaged wake characteristics of each turbine in vertical cross-sections at various downstream positions. First, the wake recovery of the upstream turbine is analysed as a function of pitch amplitude and frequency, with the latter expressed by the dimensionless Strouhal number. Next, the cascading effect of upstream turbine actuation on the wake of a downstream turbine in greedy mode is examined. Finally, wind farm power gains are assessed in a three-turbine setup incorporating a virtual turbine. Compared to the baseline greedy case, improved wake recovery is observed at both the upstream and downstream turbines, solely through upstream turbine actuation across all cases. This improvement is attributed to intensified turbulent mixing driven by DIC, which induces periodic thrust oscillations at both the actively controlled upstream turbine and the passive downstream turbine. The effect is particularly pronounced at higher pitch amplitude, while differences across Strouhal number remain minor, suggesting stronger control authority through increased pitch amplitude. Despite a decrease in DIC-added wake recovery with increasing inflow turbulence, potential power gains for the wind farm persist. Overall, this study demonstrates consistent benefits and adaptability of DIC under realistic inflow conditions, highlighting its greater potential in low-turbulence environments.
- Preprint
(6884 KB) - Metadata XML
- BibTeX
- EndNote
Status: final response (author comments only)
-
RC1: 'Comment on wes-2024-171', Anonymous Referee #1, 01 Mar 2025
This paper describes an experimental study on dynamic induction control (DIC) to mitigate wind farm wake losses using physical models in a wind tunnel. The effect is found to be mostly dependent on inflow turbulence and the amplitude of the thrust oscillation. This study provides motivation for exploring DIC further for its application at full scale wind farms.
Technical comments:
- Please archive all code and data used to generate the results in a repository like Figshare or Zenodo and cite in the paper for the sake of reproducibility. This archive does not need to be cleaned up and fully-documented/user-friendly, but it should contain all files used in the study. Please also include the OpenFAST input/output files and plotting scripts used for the results shown in the appendix.
- Line 171: Is "Prandtl tube" intended to be "Pitot tube?"
- Is it possible to estimate pitch motor energy consumption to factor that into the overall energy gain?
- Line 194: Can you explain why the dataset became faulty?
- Figure 5: It would be valuable to know some indication of the uncertainty of these statistics.
- What is the mechanism by which wake recovery is enhanced with DIC, i.e., is there a difference in the mean flow structure or is this all turbulent transport? The paper mentions vortex rings, which are certainly different from isotropic homogeneous turbulence. Do these vortex rings have any significance, or is it simply a means to inject energy that breaks down into something like isotropic turbulence to increase transport?
- What is the difference between the Reynolds number of this study and a full scale wind turbine, and how might that affect the conclusions?
- Line 273: What type of low pass filter was applied?
- Line 323: The wording is confusing here. Since WT3 does not actually exist, how can it be operating in greedy mode?
- Would it be possible to optimize tip speed ratio concurrently with pitch-actuated thrust oscillation to minimize power loss at the upstream turbine?
- How are the turbines' power losses measured? Similarly, are the downstream turbine's power gains measured with REWS or from an electrical power measurement?
- Line 362: Is it true that there was improved wake recovery but no improvement in waked turbine power?
- Line 407: What about main bearing loading?
- Figure 12: Is it possible to put error bars here?
- Figure 12: Why are losses so high with St = 0.4 in ABL type-I inflow (subplot c)? Is there any evidence that this is attributable to vibration or resonance as hypothesized? Is it possibly a measurement error?
- Some better explanations of the miniature wind turbines' increased power loss would be helpful. Is there a chance OpenFAST can't capture the the unsteady aerodynamics properly? Similarly, was an unsteady aerodynamics (dynamic stall) model applied in OpenFAST?
Minor grammatical and formatting comments:
- Both "setpoint" and "set-point" are used to describe the same concept. Recommend using "setpoint" throughout.
- Line 186: "perpendiculat" should be "perpendicular."
- Line 279 (and others): The mathematical function "min" is used as the abbreviation for minutes.
Citation: https://doi.org/10.5194/wes-2024-171-RC1 -
RC2: 'Comment on wes-2024-171', Anonymous Referee #2, 14 Mar 2025
This paper presents numerical experiments of dynamic induction control (DIC) under both laminar uniform and ABL-like inflows. Results are provided for single-turbine cases with different pitching amplitudes and frequencies. The impact of DIC is studied on both the turbine response and the wake. Similar analyses are performed with a second turbine placed downstream of WT1 and operated greedily. The cascading effect of DIC from WT1 wake to WT2 wake is discussed. The potential power gains for a third turbine are eventually presented.
The paper is well written and the structure is clear, as are the literature review and methodology section. The analyses are precise and interesting elements are discussed, with added contribution to the current state of the literature. Some points could still use clarification or further discussion. Please find them below:
L.25: “No control strategy is typically implemented to mitigate wake interactions between individual turbines”: It is not common practice yet, still some turbine manufacturers have commercial products available for wind farm operators that include wake effects mitigation through wake steering.
Sec.2 Methodology: A full wake scan takes about 15s, while a DIC actuation period is about 0.27s (St=0.30). Do you believe that scanning through different instants at different locations throughout a periodic phenomenon can be an issue?
Sec.3 Results: The “three-turbine set-up” naming is a bit misleading as there is no third turbine. Maybe you can find another formulation that is more representative of the reality.
L.255: “The ABL case exhibits more pronounced wind speed deficit patches, particularly in the upper region of the velocity field, due to the inflow wind shear.” The figure shows the difference in mean wind speed between greedy control and DIC. The effect of shear is also present in the greedy case, so in a linear world, it would cancel out. Does that mean that shear and DIC interact? Can you elaborate?
L.234: “The weighted average wind speed provides a better estimate of the energy available in the wake”: Can you substantiate this claim?
Fig6: For the uniform inflow and A=1deg, the wind speed gains keep increasing with increasing Strouhal, and, compared to the other inflows, the maximum has not been reached yet. Do you have any experimental data/insight into what would be the optimal St number in the case?
Section 3.1.2, Discussion on added turbulence: TI is defined through standard deviation sigma, which accounts for any type of fluctuations in the velocity signal, whether they are coherent or random fluctuations (in the sense of triple decomposition). Increase in TI might result from purely random contribution (strictly speaking turbulence), or from coherent structures. The point is: is it really added turbulence in the wake, or is it added fluctuations because of the shedding of vortex rings? Or is it a combination of both? This comment also holds for a sentence at L.355: “enhance turbulent mixing by introducing higher levels of DIC-added local turbulence”: is it really turbulent mixing (only) or is there also a coherent structure contribution, as discussed in Munters and Meyers, 2018, and Yilmaz and Meyers, 2018, with the formation of vortex rings? Do you have any hint in the relative importance of coherent and random fluctuations when it comes to increasing TI?
Table2: Under the ABL inflows, C_T increases with DIC, and is higher for higher St numbers. How do you explain this? Is it consistent with previous studies?
Fig10: This figure shows that, globally, the wind speed gains in WT2’s wake are higher than those in WT1’s wake. Can you comment on that?
Section 3.2.2 Thrust coefficient of WT2: It could be interesting to show the thrust signal in the frequency domain for WT1 and WT2. This could highlight the peak at f_DIC generated by the actuation of WT1, and how much this peak cascades to WT2 through the wake. That might be more explicit/specific than comparing standard deviations.
Section 3.3 Wind farm power gains: For the virtual turbine, the assumption is made that its power is computed from u_REWS and C_P = 0.37. It would be valuable to validate this hypothesis with WT2, for which you have real turbine power but can also recompute virtual power from the WT1-only cases. Could you add such a comparison, maybe as an appendix, and validate the virtual turbine assumption?
Figure 12: It might be worth reminding in the caption of the figure that WT3 is virtual. Can you also comment/explain/add a reference on why the power of WT3 is significantly higher than that of WT2?
L.371: Regarding the asymmetric behavior of the wake re-energization for Type-II inflow, it might be worth considering the work of G. Yalla, K. Brown, L. Cheung, D. Houck et al. Several papers of this group discuss DIC (among other active wake mixing techniques) in realistic offshore wind conditions, i.e. with the presence of shear, turbulence and even veer. Can similarities be found in this asymmetric behavior with such contributions?
L.404: The discussion about loads comes a bit late. Probably it should already be mentioned from the paragraph before, which recalls that gains are higher for higher pitch amplitudes. It is known, from other active wake mixing techniques (eg. Taschner et al, 2023, doi 10.1088/1742-6596/2505/1/012006), that increasing pitch amplitude directly leads to increased fatigue loads. The trade-off between loads and power is inherent to those techniques.
Typesetting
L.186: "perpendiculat"
Several expressions that should displayed on a single line are split because that are at the end of a line (Fig1 caption: St=0.30; L.216: A∈{1°,2°}; L.217: Fig. 5g-1; etc). In the final version of the paper, have a final check and make sure it does not happen using ~ instead of space.
L.278: “periodic periodic”
Citation: https://doi.org/10.5194/wes-2024-171-RC2
Viewed
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 231 | 60 | 8 | 299 | 10 | 8 |
- HTML: 231
- PDF: 60
- XML: 8
- Total: 299
- BibTeX: 10
- EndNote: 8
Viewed (geographical distribution)
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1