RELIABILITY ANALYSIS OF OFFSHORE WIND TURBINE FOUNDATION BASED ON PCK-SS-SEGO

Li Zhichuan; Sun Zhaoheng; Qi Lei; Li Ning; Du Zunfeng; Zhang Qingwei

doi:10.19912/j.0254-0096.tynxb.2023-2111

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (4) : 513-521. DOI: 10.19912/j.0254-0096.tynxb.2023-2111

RELIABILITY ANALYSIS OF OFFSHORE WIND TURBINE FOUNDATION BASED ON PCK-SS-SEGO

Li Zhichuan¹, Sun Zhaoheng¹, Qi Lei¹, Li Ning¹, Du Zunfeng², Zhang Qingwei²

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Abstract

A structural reliability algorithm for small failure probability based on PC-Kriging model and active learning is proposed for reliability analysis of small probability failure events of offshore wind turbine foundation. The algorithm utilizes Subset Simulation （SS） to divide the small failure probability events into several intermediate events, and improves the fitting accuracy of the agent model for each intermediate event through active learning, so as to improve the accuracy of the failure probability solution for the intermediate events, and verifies the feasibility and high efficiency of the method through mathematical examples. Finally, the algorithm is used to carry out the structural strength reliability analysis of the conduit frame offshore wind turbine foundation in combination with the finite element analysis of the conduit frame offshore wind turbine foundation, and the calculated probability of failure is 7.6416×10^-8, which is in line with the specification requirements, and a global sensitivity analysis is carried out, which determines that the wall thickness of the pile leg is the main factor influencing the reliability of the wind turbine foundation.

Key words

reliability analysis / Kriging / active learning / jacket type offshore wind turbine foundation / small failure probabilit

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Li Zhichuan, Sun Zhaoheng, Qi Lei, Li Ning, Du Zunfeng, Zhang Qingwei. RELIABILITY ANALYSIS OF OFFSHORE WIND TURBINE FOUNDATION BASED ON PCK-SS-SEGO[J]. Acta Energiae Solaris Sinica. 2025, 46(4): 513-521 https://doi.org/10.19912/j.0254-0096.tynxb.2023-2111

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