Preprints
https://doi.org/10.5194/wes-2022-38
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-2022-38
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
23 May 2022
23 May 2022
Status: this preprint is currently under review for the journal WES.
Extreme coherent gusts with direction change – probabilistic model, yaw control and wind turbine loads
- DTU Wind Energy Dept., Technical University of Denmark, Roskilde, Denmark
- DTU Wind Energy Dept., Technical University of Denmark, Roskilde, Denmark
Abstract. Observations of large coherent fluctuations from a decade of measurements are used to define a probabilistic model of coherent gusts with direction change. The gust model provides the joint description of the gust rise time, amplitude and directional changes with a 50-year return period. In conjunction with the gust model, a yaw controller is presented in this study to investigate the load implications of the joint gust variables. These loads are compared with the design load case of the extreme coherent gust with direction change (ECD) from the IEC 61400-1 Ed.4 wind turbine safety standard. Within the5 framework of the gust model we find the return period of the ECD to be approximately 460 years. From the simulations we find that for gusts with a relatively long rise time the blade root flapwise bending moment, for example, can be reduced by including the considered yaw controller. From the extreme load comparison of the ECD and the modeled gusts we see that by including the variability in the gust parameters the load values from the modelled gusts are between 20 % and 74 % higher than the IEC gusts.
How to cite. Hannesdóttir, Á., Verelst, D. R., and Urbán, A. M.: Extreme coherent gusts with direction change – probabilistic model, yaw control and wind turbine loads, Wind Energ. Sci. Discuss. [preprint], https://doi.org/10.5194/wes-2022-38, in review, 2022.
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Ásta Hannesdóttir et al.
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Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor
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RC1: 'Comment on wes-2022-38', Anonymous Referee #1, 18 Jul 2022
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Overall comments:
- The manuscript presents results from a probabilistic model of a dataset consisting of 92 measurements of wind conditions meeting the authors’ definition of a coherent gust. The probabilistic model uses the Nataf model to create a trivariate distribution of the data in terms of rise time, direction change, and amplitude change of the gust and ISORM is used to calculate environmental surfaces (i.e., combinations of these three variables with a common mean return period). The coherent wind gusts associated with the 50-yr environmental surface are then analyzed using a model of the DTU 10MW wind turbine in HAWC2 and the results are presented in terms of several cross-sectional demands on the tower and blades of the turbine. Simulations are implemented with and without a yaw controller. Results are compared with demands calculated following the definition of a coherent gust in IEC 61400-1.
- The authors present a clear and interesting analysis of an influential load case in the design of wind turbines. Their interpretation of the analysis provides useful insight and I enjoyed reading the manuscript. I have a few recommendations to increase clarity and accuracy, but otherwise recommend this paper for publication.
Specific comments:
- Line 6, the 460 year return period is written as if this is a general finding. Please revise to clarify that this return period is specific to a particular dataset at a particular location following a particular methodology.
- Line 16, DLCs not DLC’s
- Line 18, pls replace the wording “which is the target reliability level in wind turbine design.” with something like “which is the intended recurrence period of the environmental conditions prescribed by the IEC Standard.” The target reliability level depends on load and resistance factors in addition to the intended recurrence period of the environmental conditions of the DLC.
- Lines 61-68, while the authors refer to a previous study for the detection and characterization of the coherent gusts within their dataset, it would be helpful to provide a sentence or two here describing the criteria for an event to be categorized as a gust and the way in which the three variables are calculated for each event. In particular, please provide brief information on the spatial criteria for classification as a gust and the time average used to calculate the wind speed.
- Lines 68-69, how are the wind speeds Ua and Ub averaged in time and in space? I imagine this is described comprehensively in the authors’ previous work, but it would help to provide some brief information here.
- Section 3.1, the discussion on IFORM and ISORM needs revision. On Line 88, the statement that IFORM has been shown to underestimate the exceedance probability by an order of magnitude needs conditioning, as this is not a statement that is generally true. Can the authors elaborate what they are talking about? When multiple variables are being modeled probabilistically, there are many, equally legitimate, ways to determine exceedance probabilities and recurrence periods for combinations of these variables, so I am having trouble understanding what the authors mean when they say the exceedance probability is underestimated – underestimated compared to what?
- On Line 90, the authors refer to Equation (7) to distinguish between IFORM and ISORM, however this equation, which defines a sphere with radius beta in standard normal space, is used for both IFORM and for ISORM. The difference between the methods is in how the sphere is used to define the space of variables for which probability is calculated. In IFORM, the space is defined by a plane tangent to the sphere. In ISORM, which is a new method to me, I believe the space is defined as all points outside of the sphere. Can the authors explain more clearly the differences between IFORM and ISORM given that both require use of Equation (7)?
- On Line 91, the authors refer to an exact solution for the return period using ISORM. Perhaps I am not understanding the authors’ intent here, but I don’t understand the idea of an exact solution for calculating a return period for combinations of three variables. Since multiple variables cannot be ranked unambiguously, there are many ways to calculate exceedance probability/return periods for combinations of these variables. IFORM is one way. ISORM is another way. I don’t think it’s appropriate to call either one exact. They are just different. Are the authors saying that environmental surfaces using ISORM lead to more accurate calculations of probability of structural failure? If so, this should be clarified. And, even still, calling the result generally exact is too strong of a statement since this could only be true for a specific and simple idealization of the loading given the environmental variables.
- Section 3.1.2, suggest editing the section title to emphasize IEC, e.g. “the IEC ECD” instead of “the ECD”
- The result on Line 160 should be emphasized as being calculated for one specific site using one specific methodology.
- Line 232, bending not binding.
- Line 251, at this point the meaning of “load channels” was not clear to me. I eventually figured it out after seeing Table 2. It could be helpful to define this term earlier.
- Line 257, pitch not pith.
- Line 271, as I was thinking about the results for TT_yaw, I was curious how much of the loading is inertial as the yaw controller accelerates the rotor. On a related note, does the yawing of the spinning rotor during an ECD cause a significant gyrotorque? This may be outside of scope, but, it my opinion, it would be interesting to provide some discussion on the influence of inertial loading compared to aerodynamic loading for this condition
- Table 2, suggest dropping a couple of significant figures from the reported moments.
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AC1: 'Reply on RC1', Ásta Hannesdóttir, 12 Sep 2022
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Thank you very much for a good and constructive review of our paper. We will address your comments in our next revision of the paper when all reviewer comments are received.
Ásta Hannesdóttir et al.
Ásta Hannesdóttir et al.
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Short summary
In this work we use observations of large coherent fluctuations to define a probabilistic gust model. The gust model provides the joint description of the gust rise time, amplitude and direction change. We perform load simulations with a coherent gust according to the wind turbine safety standard and with the probabilistic gust model. A comparison of the simulated loads shows that the loads from the probabilistic gust model can be significantly higher due to variability in the gust parameters.
In this work we use observations of large coherent fluctuations to define a probabilistic gust...