the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 24 Mar 2025
FastLE: A New Load Extrapolation Method for Site-specific Wind Turbines Using the Load Distribution Meta Model
Abstract. To ensure the safety of wind turbines at specific sites, IEC 61400-1 mandates the extrapolation of loads as a key requirement. Given the variability in wind parameters across different turbine sites, particularly in complex terrains, this task demands significant computational resources for simulations. However, the method recommended in the standard fall short of providing comprehensive assessments and rapid iterations necessary for all turbine locations within wind farm optimization designs. This paper presents a rapid load extrapolation method, named FastLE, which is based on a load distribution meta-model and tailored for specific sites. Based on 20 test cases, the blade root out-of-plane bending moment (OOPBM) for a 50-year return period was calculated using both the IEC method and the FastLE method introduced in this paper. Through comparative analysis, the mean APE is only 3.165 %, and the computation time for a single calculation has been reduced from 20 hours to less than 1 second. The results show that the FastLE method can complete load extrapolation calculations for wind turbines in seconds with high accuracy. This makes it suitable for ensuring structural integrity during iterations of wind farm layout optimization or turbine type optimization, thereby reducing the safety risks associated with wind turbines.
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EC1: 'Comment on wes-2025-39', Nikolay Dimitrov, 24 Mar 2025
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Dear authors, thank you for submitting this interesting paper. I have a few comments which I hope will be complementary to the upcoming reviews.
- The authors mention an impressive dataset of 541 meteorological towers in a specific region. Some more details would be relevant in order to understand if the data are comparable – i.e., how does the terrain differ among the various met mast locations, are the measurement heights the same, are the instruments the same (cup anemometers, sonic anemometers, lidars)?
- The IEC 61400, ed. 4 standard allows several different approaches to extrapolation, including avoiding the extrapolation altogether by introducing a higher safety factor. It will be useful if the authors could study/compare these different extrapolation approaches in the context of their proposed methodology.
- One significant challenge in the “fitting before aggregation” method is that the distribution fitting on a few values is not very robust, and a few outliers or bad fits can distort the aggregated result. It would be good to check the confidence in the aggregated distribution predictions – for example by doing multiple local distribution fits by bootstrapping the block maxima.
- There is a dependency between the shape and scale parameters in a Weibull distribution fit (if you choose a value of one parameter, it will define what is the value of the other parameter that best represents the data set). Therefore, fitting separate meta models for the scale and shape parameters of the Weibull distribution may limit the accuracy of the results. In the current manuscript, it doesn’t get clear if the authors fit one single MLP model with two outputs, or two separate models? Please discuss.
Citation: https://doi.org/10.5194/wes-2025-39-EC1 -
AC1: 'Reply on EC1', Shanshan Guo, 27 Mar 2025
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Please check the reply details in the attached pdf document.
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RC1: 'Comment on wes-2025-39', Anonymous Referee #1, 19 May 2025
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The paper deals with a rapid evaluation of the extreme loads using extrapolation methods. currently the extrapolation requires a significant number of simulations to provide sufficient samples of extreme loads in order to perform the extrapolation procedure. The manuscript uses a machine learning based meta model to accelerate this process while providing extrapolation result with uncertainties comparable to those using aeroelastic simulations. One important question that needs to be clarified is what is the value of the meta model compared to the many load surrogate models that are available in the literature. Afterall the main time saving is coming from the meta load model which is essentially another load surrogate model.
1- when comparing the time saving, how would the authors account the time and efforts needed to produce the data needed to train the meta model. since this would be necessary each time the turbine model or turbine properties have been changes, which is often the case in the design iteration phase. Normally the 50 years return value for extreme load is a design value based on generic wind class or site specific value for certain class of sites, for example typhoon or hurricane affected area. It is usually not needed to perform load extrapolation for each of the wind turbine in a wind farm. Once it has been identified which turbine in the wind farm has the highest extreme loads, one needs only to perform the load extrapolation for the worst case. It is rather unlikely that optimization for extreme loads will be performed for every single turbine. Moreover, it is not clear from the beginning of the design, whether fatigue or extreme load will be the design driver. Therefore, the usefulness and time saving should be considered with these points in mind.
2- In page three, line 84, the word inflow angle is mentioned. In this case, it is referred to the yaw angle between the rotor plane with the incoming wind, that is, the yaw misalignment angle, if the reviewer understands it correctly. Inflow angle is used in the aerodynamics mainly for the angle of the velocity triangle at the airfoil, between the tangential velocity caused by the rotation of the rotor and the incoming wind velocity. The use of the word inflow angle can cause some confusion as this is not used normally in this context.
3- Page 4. which is the shear model used and how is the shear value defined, please elaborate.
4- Figure1, the distribution of the air density looks bi-modal, when sampling the distribution, did the authors take the empirical distribution or the fitted bi-modal distribution
5- Table 1 why is the inflow angle changes from -0.78 to 13.464 degrees (there is no need to go beyond the first digit for this angle, the turbine yaw controller is not that precise) , what about the variation in the negative angle. the loading on the wind turbine is not symmetrical around the yaw angle, negative and positive yaw angles can produce very different loads.
6- Table 2 change RMP to RPM
7- page 7 what is the definition of In plane and out of plane bending moment here. It looks like the authors is using the flapwise bending moment and not the out of plane bending moment of the blade. Once the blade starts pitching after reaching the rated wind speed, the the OOP bending moment and flapwise bending moment are no longer the same.
8- Equation 6, this equation assumes that the 10 minutes wind speeds are independent, which is clearly not the case.
9- page 9, line 171, the authors divided the data into three categories, high wind speed range above 10 m/s , low wind speed range below 10 m/s and full wind speed range, which wind speed would be full wind speed range have?
10- Figure 6 why are the log-normal performed so poorly in QQ plot
11- Table 3, there is not need to have numbers with 9 digits after the decimal point, there are a lot of uncertainties
11- Figure 10, how ar ehte importance of the hyperparameters determined?
12- page 9 line 176, so if the low wind speeds contribute so little to the tail of the distribution, then why simulate them at all.
13- instead of local distribution, maybe it is better to refer them as local maxima, or local peaks distribution.
14- Table 5, the simulation time is 600seconds, what about the transient at the beginning of the simulation, are they removed ?
Citation: https://doi.org/10.5194/wes-2025-39-RC1 -
AC2: 'Reply on RC1', Shanshan Guo, 28 May 2025
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Please check the reply details in the Supplement, which is a pdf document.
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AC2: 'Reply on RC1', Shanshan Guo, 28 May 2025
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