Transfer learning and direct probability integral method based reliability analysis for offshore wind turbine blades under multi-physics coupling

Xiaoling Zhang, Kejia Zhang, Xiao Yang, Tiago Fazeres-Ferradosa and Shun-Peng Zhu

Renewable Energy, 2023, vol. 206, issue C, 552-565

Abstract: Reliability of blades has a profound impact on both serviceability and safety of offshore wind turbines. Reliability estimation of wind turbine blade is a complex and time-consuming problem with multi-physics coupling and multi-failure modes correlation. Developing physics-of-failure modeling and reliability analysis methods with high efficiency and accuracy is a long-term challenge. In this work, a high availability and cost-effectiveness reliability estimation framework for offshore wind turbine blade by combining transfer learning (TL) and direct probability integral method (DPIM) is proposed. Extensive performance simulation of offshore wind turbine blade is a complex and necessary task, this paper develops a new adaptive sampling strategy to improve the validity of sample selection in the design space; On this basis, a physics-of-failure surrogate modeling approach is proposed by introducing TL method to fuse two kinds of multi-physics coupling analysis data, then the performance of all critical loads can be predicted efficiently in advance to provide a reliable design; Further, this paper provides an efficient reliability estimation method for offshore wind turbine blades by combining DPIM and surrogate model. Finally, the validity of the proposed approach is illustrated by numerical example and offshore wind turbine blade reliability estimation. The proposed framework provides a cost-effective alternative to higher loads simulation efforts and safety factors selection.

Keywords: Offshore wind turbine blade; Adaptive sampling strategy; Reliability; Transfer learning; Direct probability integral (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:206:y:2023:i:c:p:552-565

DOI: 10.1016/j.renene.2023.02.026

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