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Wind turbine blade icing detection: a federated learning approach

Author

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  • Cheng, Xu
  • Shi, Fan
  • Liu, Yongping
  • Liu, Xiufeng
  • Huang, Lizhen

Abstract

Wind farms are often located at high latitudes, which entails a high risk of icing for wind turbine blades. Traditional anti-icing methods rely primarily on manual observation, the use of special materials, or external sensors/tools, but these methods are limited by human experience, additional costs, and understanding of the mechanical mechanism. Model-based approaches rely heavily on prior knowledge and are subject to misinterpretation. Data-driven approaches can deliver promising solutions but require large datasets for training, which might face significant challenges with respect to data management, e.g., privacy protection and ownership. To address these issues, this paper proposes a federated learning (FL) based model for blade icing detection. The proposed approach first creates a prototype-based model for each client and then aggregates all client models into a globally weighted model. The clients use a prototype-based modeling method to address the data imbalance problem, while using the FL-based learning method to ensure data security and safety. The proposed model is comprehensively evaluated using data from two wind farms, with 70 wind turbines. The results validate the effectiveness of the proposed prototype-based client model for feature extraction, and the superiority over the five baselines in terms of icing detection accuracy. In addition, the experiment demonstrates the promising result of online blade icing detection, with almost 100% accuracy.

Suggested Citation

  • Cheng, Xu & Shi, Fan & Liu, Yongping & Liu, Xiufeng & Huang, Lizhen, 2022. "Wind turbine blade icing detection: a federated learning approach," Energy, Elsevier, vol. 254(PC).
  • Handle: RePEc:eee:energy:v:254:y:2022:i:pc:s0360544222013445
    DOI: 10.1016/j.energy.2022.124441
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    References listed on IDEAS

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    Cited by:

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    2. 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).
    3. 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.
    4. 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).
    5. 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.
    6. 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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