Class-Imbalanced Spatial–Temporal Feature Learning for Blade Icing Recognition of Wind Turbine-Reference-Cited by-同舟云学术

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Class-Imbalanced Spatial–Temporal Feature Learning for Blade Icing Recognition of Wind Turbine

Published:2024-08 Issue:8 Volume:20 Page:10249-10258
ISSN:1551-3203
Container-title:IEEE Transactions on Industrial Informatics
language:
Short-container-title:IEEE Trans. Ind. Inf.

Author:

Wang Renfang¹^ORCID,Qiu Hong¹^ORCID,Jiang Guoqian²^ORCID,Liu Xiufeng³^ORCID,Cheng Xu³^ORCID

Affiliation:

1. School of Big Data and Software, Zhejiang Wanli University, Ningbo, China

2. School of Electrical Engineering, Yanshan University, Qinhuangdao, China

3. Department of Technology, Management and Economics, Technical University of Denmark, Kongens Lyngby, Denmark

Funder

National Natural Science Foundation of China

Science and Technology Plan for Zhejiang Province

Ningbo Municipal Science and Technology

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/9424/10623532/10522945.pdf?arnumber=10522945

Reference30 articles.

1. Wind turbine blade icing detection: a federated learning approach

2. A Multilevel Convolutional Recurrent Neural Network for Blade Icing Detection of Wind Turbine

3. Temporal Attention Convolutional Neural Network for Estimation of Icing Probability on Wind Turbine Blades

4. Numerical simulation of rime ice on NREL Phase VI blade

5. Prediction of wind turbine blade icing fault based on selective deep ensemble model

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Multilevel Signal Decomposition Layer-Specific Residual Network for Blade Icing Prediction;IEEE Signal Processing Letters;2024

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