Copula-function-based wind turbine blade fatigue reliability modeling and assessment with consideration of failure correlation

Liang Tu; Zhenfeng He; Yongjie Li; Zheng Liu; Ruizhi Tang

doi:10.1117/12.3025466

28 February 2024 Copula-function-based wind turbine blade fatigue reliability modeling and assessment with consideration of failure correlation

Liang Tu, Zhenfeng He, Yongjie Li, Zheng Liu, Ruizhi Tang

Author Affiliations +

Proceedings Volume 13071, International Conference on Mechatronic Engineering and Artificial Intelligence (MEAI 2023); 130712A (2024) https://doi.org/10.1117/12.3025466
Event: International Conference on Mechatronic Engineering and Artificial Intelligence (MEAI 2023), 2023, Shenyang, China

Abstract

As a frontier component in capturing wind energy, the wind turbine blade (WTB) plays a key role in the efficient performance of a wind turbine. The WTB will suffer various failures or different degrees of damage in its operation due to the harsh environmental conditions, thus the WTB fatigue reliability study is a critical topic. The WTB is a complex structure, composed of leading edge, trailing edge, leaf root, web, middle, main beam, etc., and the failures of each element is correlated. This paper proposes a WTB fatigue reliability modelling and assessment method with consideration of failure correlation. In particular, based on finite element method, the time to failure of each element is obtained and the lifetime distribution is determined. Then, Copula function is introduced to quantify and characterize the failure correlation of the elements, and a fatigue reliability model is constructed for the WTB. The effectiveness of the proposed model is explained by a contrastive analysis with the results of independence assumption.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Liang Tu, Zhenfeng He, Yongjie Li, Zheng Liu, and Ruizhi Tang "Copula-function-based wind turbine blade fatigue reliability modeling and assessment with consideration of failure correlation", Proc. SPIE 13071, International Conference on Mechatronic Engineering and Artificial Intelligence (MEAI 2023), 130712A (28 February 2024); https://doi.org/10.1117/12.3025466

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