77 citations as recorded by crossref.

1. Aeroelastic load validation in wake conditions using nacelle-mounted lidar measurements D. Conti et al. 10.5194/wes-5-1129-2020
2. Surrogate models for predicting stall-induced vibrations on wind turbine blades C. Santhanam et al. 10.1088/1742-6596/2265/3/032005
3. Improving site-dependent power curve prediction accuracy using regression trees S. Barber & H. Nordborg 10.1088/1742-6596/1618/6/062003
4. Virtual sensors for wind turbines with machine learning‐based time series models N. Dimitrov & T. Göçmen 10.1002/we.2762
5. Fatigue reliability analysis of floating offshore wind turbines under the random environmental conditions based on surrogate model G. Zhao et al. 10.1016/j.oceaneng.2024.119686
6. Power Enhancement of a Vertical Axis Wind Turbine Equipped with an Improved Duct M. Ranjbar et al. 10.3390/en14185780
7. Statistical modeling of fully nonlinear hydrodynamic loads on offshore wind turbine monopile foundations using wave episodes and targeted CFD simulations through active sampling S. Guth et al. 10.1002/we.2880
8. Given-data probabilistic fatigue assessment for offshore wind turbines using Bayesian quadrature E. Fekhari et al. 10.1017/dce.2023.27
9. Probabilistic surrogates for flow control using combined control strategies C. Debusscher et al. 10.1088/1742-6596/2265/3/032110
10. On long-term fatigue damage estimation for a floating offshore wind turbine using a surrogate model D. Liu et al. 10.1016/j.renene.2024.120238
11. Optimizing wind farm control through wake steering using surrogate models based on high-fidelity simulations P. Hulsman et al. 10.5194/wes-5-309-2020
12. Probabilistic surrogate modeling of damage equivalent loads on onshore and offshore wind turbines using mixture density networks D. Singh et al. 10.5194/wes-9-1885-2024
13. Physics-Informed Neural Network surrogate model for bypassing Blade Element Momentum theory in wind turbine aerodynamic load estimation S. Baisthakur & B. Fitzgerald 10.1016/j.renene.2024.120122
14. Surrogate Modeling and Aeroelastic Analysis of a Wind Turbine with Down-Regulation, Power Boosting, and IBC Capabilities V. Pettas & P. Cheng 10.3390/en17061284
15. Variable importance analysis of wind turbine extreme responses with Shapley value explanation X. Zhang & N. Dimitrov 10.1016/j.renene.2024.121049
16. Surrogate model for fast simulation of turbine loads in wind farms E. Bossanyi 10.1088/1742-6596/2265/4/042038
17. A study of Stall-Induced Vibrations using Surrogate-Based Optimization C. Santhanam et al. 10.1016/j.renene.2023.05.054
18. Data-driven surrogate model for wind turbine damage equivalent load R. Haghi & C. Crawford 10.5194/wes-9-2039-2024
19. From shear to veer: theory, statistics, and practical application M. Kelly & M. van der Laan 10.5194/wes-8-975-2023
20. Grand challenges in the design, manufacture, and operation of future wind turbine systems P. Veers et al. 10.5194/wes-8-1071-2023
21. Influence of wind-wave characteristics on floating wind turbine loads: A sensitivity analysis across different floating concepts L. Reddy et al. 10.1088/1742-6596/2767/6/062030
22. Østerild: A natural laboratory for atmospheric turbulence A. Peña 10.1063/1.5121486
23. Incorporation of floater rotation and displacement in a static wind farm simulator R. Riva et al. 10.1088/1742-6596/2767/6/062019
24. Wind turbine load estimation using machine learning and transfer learning G. Xu et al. 10.1088/1742-6596/2265/3/032108
25. Probabilistic surrogate modeling of offshore wind-turbine loads with chained Gaussian processes D. Singh et al. 10.1088/1742-6596/2265/3/032070
26. Statistical impact of wind-speed ramp events on turbines, via observations and coupled fluid-dynamic and aeroelastic simulations M. Kelly et al. 10.5194/wes-6-1227-2021
27. Wind load assessment in marine and offshore engineering standards H. Kozmar et al. 10.1016/j.oceaneng.2022.110872
28. Wind turbine load validation in wakes using wind field reconstruction techniques and nacelle lidar wind retrievals D. Conti et al. 10.5194/wes-6-841-2021
29. Operational-based annual energy production uncertainty: are its components actually uncorrelated? N. Bodini & M. Optis 10.5194/wes-5-1435-2020
30. From wind conditions to operational strategy: optimal planning of wind turbine damage progression over its lifetime N. Requate et al. 10.5194/wes-8-1727-2023
31. Surrogate models for parameterized representation of wake‐induced loads in wind farms N. Dimitrov 10.1002/we.2362
32. The surrogate model for short-term extreme response prediction based on ANN and Kriging algorithm G. Zhao et al. 10.1016/j.apor.2024.104196
33. Global trends in the performance of large wind farms based on high-fidelity simulations S. Andersen et al. 10.5194/wes-5-1689-2020
34. Atmospheric condition identification in multivariate data through a metric for total variation N. Hamilton 10.5194/amt-13-1019-2020
35. From SCADA to lifetime assessment and performance optimization: how to use models and machine learning to extract useful insights from limited data N. Dimitrov & A. Natarajan 10.1088/1742-6596/1222/1/012032
36. Retrospective de‐trending of wind site turbulence using machine learning F. Tough & E. Hart 10.1002/we.2720
37. Enhanced shear and veer in the Taiwan Strait during typhoon passage S. Müller et al. 10.1088/1742-6596/2767/9/092030
38. Efficient layout optimization of offshore wind farm based on load surrogate model and genetic algorithm X. Zhang et al. 10.1016/j.energy.2024.133106
39. Bivariate statistics of floating offshore wind turbine dynamic response under operational conditions X. Xu et al. 10.1016/j.oceaneng.2022.111657
40. Research on dynamic response prediction of semi-submersible wind turbine platform in real sea test model based on machine learning H. Jiang et al. 10.1016/j.apor.2023.103808
41. A wind turbine digital shadow with tower and blade degrees of freedom - Preliminary results and comparison with a simple tower fore-aft model H. Hoghooghi et al. 10.1088/1742-6596/2767/3/032026
42. Augmented Kalman filter with a reduced mechanical model to estimate tower loads on a land-based wind turbine: a step towards digital-twin simulations E. Branlard et al. 10.5194/wes-5-1155-2020
43. Long-term fatigue damage assessment for a floating offshore wind turbine under realistic environmental conditions X. Li & W. Zhang 10.1016/j.renene.2020.06.043
44. Wind Turbine Damage Equivalent Load Assessment Using Gaussian Process Regression Combining Measurement and Synthetic Data R. Haghi et al. 10.3390/en17020346
45. Real-Time Hybrid Simulation with Polynomial Chaos NARX Modeling for Seismic Response Evaluation of Structures Subjected to Stochastic Ground Motions X. Gao et al. 10.1061/(ASCE)ST.1943-541X.0003451
46. Reliability‐based layout optimization in offshore wind energy systems C. Clark et al. 10.1002/we.2664
47. Analysis of the influence of climate change on the fatigue lifetime of offshore wind turbines using imprecise probabilities C. Hübler & R. Rolfes 10.1002/we.2572
48. Reaction loads analysis of floating offshore wind turbines: Methods and applications in the modal-based modeling framework C. Høeg & Z. Zhang 10.1016/j.oceaneng.2022.112952
49. Efficient fatigue damage estimation of offshore wind turbine foundation under wind-wave actions T. Li et al. 10.1016/j.jcsr.2024.108903
50. Virtual fatigue diagnostics of wake-affected wind turbine via Gaussian Process Regression L. Avendaño-Valencia et al. 10.1016/j.renene.2021.02.003
51. FarmConners market showcase results: wind farm flow control considering electricity prices K. Kölle et al. 10.5194/wes-7-2181-2022
52. Improving Site-Dependent Wind Turbine Performance Prediction Accuracy Using Machine Learning S. Barber et al. 10.1115/1.4053513
53. Probabilistic temporal extrapolation of fatigue damage of offshore wind turbine substructures based on strain measurements C. Hübler & R. Rolfes 10.5194/wes-7-1919-2022
54. Modelling for Digital Twins—Potential Role of Surrogate Models Á. Bárkányi et al. 10.3390/pr9030476
55. Uncertainty propagation and sensitivity analysis of an artificial neural network used as wind turbine load surrogate model L. Schröder et al. 10.1088/1742-6596/1618/4/042040
56. Estimation of probability distribution of long-term fatigue damage on wind turbine tower using residual neural network H. Bai et al. 10.1016/j.ymssp.2023.110101
57. A surrogate model approach for associating wind farm load variations with turbine failures L. Schröder et al. 10.5194/wes-5-1007-2020
58. Data-driven time series forecasting of offshore wind turbine loads H. Muhammad Amri et al. 10.1088/1742-6596/2767/5/052060
59. Surrogate model uncertainty in wind turbine reliability assessment R. Slot et al. 10.1016/j.renene.2019.11.101
60. On the extension of streamwise turbulence intensity profile beyond the atmospheric surface layer under neutral to unstable stratifications M. Mataji 10.1016/j.jweia.2022.105100
61. Probabilistic fatigue surrogate model of bimodal tension process for a semi-submersible platform Y. Zhao & S. Dong 10.1016/j.oceaneng.2020.108501
62. Optimal design of experiments for computing the fatigue life of an offshore wind turbine based on stepwise uncertainty reduction A. Cousin et al. 10.1016/j.strusafe.2024.102483
63. Surrogate models for the blade element momentum aerodynamic model using non-intrusive polynomial chaos expansions R. Haghi & C. Crawford 10.5194/wes-7-1289-2022
64. Wind farm layout optimization with load constraints using surrogate modelling R. Riva et al. 10.1088/1742-6596/1618/4/042035
65. Conditional variational autoencoders for probabilistic wind turbine blade fatigue estimation using Supervisory, Control, and Data Acquisition data C. Mylonas et al. 10.1002/we.2621
66. Data‐driven modeling for fatigue loads of large‐scale wind turbines under active power regulation J. Yang et al. 10.1002/we.2589
67. A digital twin solution for floating offshore wind turbines validated using a full-scale prototype E. Branlard et al. 10.5194/wes-9-1-2024
68. Efficient Loads Surrogates for Waked Turbines in an Array K. Shaler et al. 10.1088/1742-6596/2265/3/032095
69. Sensitivity of fatigue reliability in wind turbines: effects of design turbulence and the Wöhler exponent S. Mozafari et al. 10.5194/wes-9-799-2024
70. Polymorphic uncertainty in met-ocean conditions and the influence on fatigue loads C. Hübler et al. 10.1088/1742-6596/1669/1/012005
71. Modelling of turbine power and local wind conditions in wind farm using an autoencoder neural network S. Dou & N. Dimitrov 10.1088/1742-6596/2265/3/032069
72. A Machine Learning Method for Modeling Wind Farm Fatigue Load Y. Miao et al. 10.3390/app12157392
73. A control-oriented load surrogate model based on sector-averaged inflow quantities: capturing damage for unwaked, waked, wake-steering and curtailed wind turbines A. Guilloré et al. 10.1088/1742-6596/2767/3/032019
74. Sensitivity analysis of the effect of wind characteristics and turbine properties on wind turbine loads A. Robertson et al. 10.5194/wes-4-479-2019
75. System-level design studies for large rotors D. Zalkind et al. 10.5194/wes-4-595-2019
76. Wind turbine site-specific load estimation using artificial neural networks calibrated by means of high-fidelity load simulations L. Schröder et al. 10.1088/1742-6596/1037/6/062027
77. Risk-based approach for rational categorization of damage observations from wind turbine blade inspections N. Dimitrov 10.1088/1742-6596/1037/4/042021