Gaussian process regression modeling of wind turbines lightning incidence with LLS information
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DOI: 10.1016/j.renene.2019.07.050
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- Stetco, Adrian & Dinmohammadi, Fateme & Zhao, Xingyu & Robu, Valentin & Flynn, David & Barnes, Mike & Keane, John & Nenadic, Goran, 2019. "Machine learning methods for wind turbine condition monitoring: A review," Renewable Energy, Elsevier, vol. 133(C), pages 620-635.
- Zhou, Qibin & Liu, Canxiang & Bian, Xiaoyan & Lo, Kwok L. & Li, Dongdong, 2018. "Numerical analysis of lightning attachment to wind turbine blade," Renewable Energy, Elsevier, vol. 116(PA), pages 584-593.
- Xishan Wen & Lu Qu & Yu Wang & Xiaoyue Chen & Lei Lan & Tianjun Si & Jianwei Xu, 2016. "Effect of Wind Turbine Blade Rotation on Triggering Lightning: An Experimental Study," Energies, MDPI, vol. 9(12), pages 1-15, December.
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- Petar Sarajcev & Antun Meglic & Ranko Goic, 2021. "Lightning Overvoltage Protection of Step-Up Transformer Inside a Nacelle of Onshore New-Generation Wind Turbines," Energies, MDPI, vol. 14(2), pages 1-20, January.
- Wang, Jian & Gao, Shibin & Yu, Long & Zhang, Dongkai & Xie, Chenlin & Chen, Ke & Kou, Lei, 2023. "Data-driven lightning-related failure risk prediction of overhead contact lines based on Bayesian network with spatiotemporal fragility model," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
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Keywords
Bayesian statistics; Gaussian process regression; Lightning; LLS; Machine learning; Wind turbine;All these keywords.
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