11 citations as recorded by crossref.

1. Estimation of Hub Center Loads for Individual Pitch Control for Wind Turbines Based on Tower Loads and Machine Learning S. Kiyoki et al. 10.3390/electronics13183648
2. Farm‐wide interface fatigue loads estimation: A data‐driven approach based on accelerometers F. de N Santos et al. 10.1002/we.2888
3. An identification method of floating wind turbine tower responses using deep learning technology in the monitoring system Z. Wang et al. 10.1016/j.oceaneng.2022.112105
4. Model-free dynamic estimation of fore-aft and side-to-side wind turbine tower deflections M. Saavedra et al. 10.1063/5.0216741
5. Machine Learning-Based Prediction of 2 MW Wind Turbine Tower Loads During Power Production Based on Nacelle Behavior S. Kiyoki et al. 10.3390/en18010216
6. Results of fatigue measurement campaign on XL monopiles and early predictive models F. Santos et al. 10.1088/1742-6596/2265/3/032092
7. 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
8. Long-term fatigue estimation on offshore wind turbines interface loads through loss function physics-guided learning of neural networks F. de N Santos et al. 10.1016/j.renene.2023.01.093
9. Monitoring system framework design for floating wind turbine using the deep learning technology and tower response identification considering sensor optimization Z. Wang et al. 10.1016/j.oceaneng.2024.117316
10. Data-driven farm-wide fatigue estimation on jacket-foundation OWTs for multiple SHM setups F. d N Santos et al. 10.5194/wes-7-299-2022
11. Data-driven surrogate model for wind turbine damage equivalent load R. Haghi & C. Crawford 10.5194/wes-9-2039-2024