Jiang, Guoqian https://orcid.org/0000-0002-1813-8249
Li, Wenyue
Bai, Jiarong
He, Qun
Xie, Ping https://orcid.org/0000-0001-5878-087X
Funding for this research was provided by:
Natural Scientific Foundation of China (61803329)
Hebei Provincial Department of Human Resources and Social Security (C20210326)
S & T Program of Hebei Province (19214306D)
Natural Scientific Foundation of Hebei Province (F2021203009)
Article Title: SCADA data-driven blade icing detection for wind turbines: an enhanced spatio-temporal feature learning approach
Journal Title: Measurement Science and Technology
Article Type: paper
Copyright Information: © 2023 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved.
Publication dates
Date Received: 2022-10-04
Date Accepted: 2023-01-31
Online publication date: 2023-02-14