Virtual fatigue diagnostics of wake-affected wind turbine via Gaussian Process Regression
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DOI: 10.1016/j.renene.2021.02.003
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Cited by:
- de N Santos, Francisco & D’Antuono, Pietro & Robbelein, Koen & Noppe, Nymfa & Weijtjens, Wout & Devriendt, Christof, 2023. "Long-term fatigue estimation on offshore wind turbines interface loads through loss function physics-guided learning of neural networks," Renewable Energy, Elsevier, vol. 205(C), pages 461-474.
- Ren, Chao & Xing, Yihan, 2023. "AK-MDAmax: Maximum fatigue damage assessment of wind turbine towers considering multi-location with an active learning approach," Renewable Energy, Elsevier, vol. 215(C).
- Cuauhtemoc Acosta Lúa & Domenico Bianchi & Salvador Martín Baragaño & Mario Di Ferdinando & Stefano Di Gennaro, 2023. "Robust Nonlinear Control of a Wind Turbine with a Permanent Magnet Synchronous Generator," Energies, MDPI, vol. 16(18), pages 1-19, September.
- Moynihan, Bridget & Tronci, Eleonora M. & Hughes, Michael C. & Moaveni, Babak & Hines, Eric, 2024. "Virtual sensing via Gaussian Process for bending moment response prediction of an offshore wind turbine using SCADA data," Renewable Energy, Elsevier, vol. 227(C).
- Liu, Ding Peng & Ferri, Giulio & Heo, Taemin & Marino, Enzo & Manuel, Lance, 2024. "On long-term fatigue damage estimation for a floating offshore wind turbine using a surrogate model," Renewable Energy, Elsevier, vol. 225(C).
- Chen, Peng & Han, Dezhi, 2022. "Effective wind speed estimation study of the wind turbine based on deep learning," Energy, Elsevier, vol. 247(C).
- Dao, Fang & Zeng, Yun & Qian, Jing, 2024. "Fault diagnosis of hydro-turbine via the incorporation of bayesian algorithm optimized CNN-LSTM neural network," Energy, Elsevier, vol. 290(C).
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Keywords
Wind turbine; Fatigue; Wake; Uncertainty; Bayesian Gaussian process regression; Virtual sensing;All these keywords.
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