Wind turbine blade breakage detection based on environment-adapted contrastive learning

Shilin Sun, Qi Li, Wenyang Hu, Zhongchao Liang, Tianyang Wang and Fulei Chu

Renewable Energy, 2023, vol. 219, issue P2

Abstract: Blade breakages in wind turbines are serious threats to reliable power generation. However, blade breakage detection is still challenging owing to varying circumstances and the questionable assumption that distinct health conditions result in inherently separable measurements. To address these challenges, a novel method is proposed for blade breakage detection based on environment-adapted contrastive learning. Specifically, nonparametric regression models between operational and environmental variables are established, and the variable adjustment is conducted to suppress the influence of variational circumstances on SCADA measurements. In addition, an improved strategy of contrastive learning is developed to learn valid data representations, guaranteeing the assumed separability of different samples. Furthermore, normal models of healthy operations are constructed, and online monitoring is implemented based on likelihood estimation and control charts. Actual data from commercial wind farms have been employed to demonstrate the effectiveness and superiority of the proposed method. Experimental results confirm that the correlation degree between operational and environmental variables can be reduced by 28.7 %, and blade breakages can be detected more than 23 h in advance.

Keywords: Anomaly detection; Artificial intelligence; Condition monitoring; Contrastive learning; Wind turbine blade (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0960148123014027
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:219:y:2023:i:p2:s0960148123014027

DOI: 10.1016/j.renene.2023.119487

Access Statistics for this article

Renewable Energy is currently edited by Soteris A. Kalogirou and Paul Christodoulides

More articles in Renewable Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().