Wind turbine blade icing detection: a federated learning approach
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DOI: 10.1016/j.energy.2022.124441
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Cited by:
- Chen, Zhiqiang & Li, Jianbin & Cheng, Long & Liu, Xiufeng, 2023. "Federated-WDCGAN: A federated smart meter data sharing framework for privacy preservation," Applied Energy, Elsevier, vol. 334(C).
- Tao, Cheng & Tao, Tao & He, Shukai & Bai, Xinjian & Liu, Yongqian, 2024. "Wind turbine blade icing diagnosis using B-SMOTE-Bi-GRU and RFE combined with icing mechanism," Renewable Energy, Elsevier, vol. 221(C).
- Chang Cai & Jicai Guo & Xiaowen Song & Yanfeng Zhang & Jianxin Wu & Shufeng Tang & Yan Jia & Zhitai Xing & Qing’an Li, 2023. "Review of Data-Driven Approaches for Wind Turbine Blade Icing Detection," Sustainability, MDPI, vol. 15(2), pages 1-20, January.
- Wang, Bingkai & Sun, Wenlei & Wang, Hongwei & Xu, Tiantian & Zou, Yi, 2024. "Research on rapid calculation method of wind turbine blade strain for digital twin," Renewable Energy, Elsevier, vol. 221(C).
- Miguel Moreira & Frederico Rodrigues & Sílvio Cândido & Guilherme Santos & José Páscoa, 2023. "Development of a Background-Oriented Schlieren (BOS) System for Thermal Characterization of Flow Induced by Plasma Actuators," Energies, MDPI, vol. 16(1), pages 1-17, January.
- Liu, Yixing & Liu, Bo & Guo, Xiaoyu & Xu, Yiqiao & Ding, Zhengtao, 2023. "Household profile identification for retailers based on personalized federated learning," Energy, Elsevier, vol. 275(C).
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Keywords
Wind turbine; Blade icing detection; Federated learning; Imbalanced learning; Time series classification;All these keywords.
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