A Fully Coupled Sensitivity Analysis Framework for Offshore Wind Turbines Based on an XGBoost Surrogate Model and the Interpretation of SHAP

To advance global sustainability and meet climate targets, the development of reliable renewable energy infrastructure is paramount. Offshore wind energy is a key factor in achieving this goal, and ensuring its operational efficiency requires a deep understanding of the sources of uncertainty faced by offshore wind turbines (OWTs). This study proposes and implements an integrated framework for sensitivity analysis (SA) to investigate the key sources of uncertainty influencing the dynamic response of an OWT. This framework is based on the XGBoost surrogate model and Sobol’s method, aiming to efficiently and accurately quantify the impact of various uncertain parameters. A key methodological novelty lies in the integrated use of Sobol’s method and SHapley Additive exPlanations (SHAP), which provides a unique cross-validating mechanism for the sensitivity results. This study demonstrates the strongly condition-dependent nature of the OWT’s sensitivity characteristics by analyzing design load cases. The results indicate that wind speed is the dominant factor influencing the structural response under normal operating conditions. In contrast, under extreme shutdown conditions, the response of the OWT is primarily governed by the physical and material properties of the structure. In addition, the high consistency between the results of SHAP technology and the SA results obtained by Sobol’s method confirms the reliability of the proposed framework. The identified key sources of uncertainty provide direct practical insights for design optimization and reliability assessment of OWTs.

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Publication Details

Journal
Sustainability
Published
2025-10-17
DOI
https://doi.org/10.3390/su17209227
Primary Topic
Wind Energy Research and Development
Type
article
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article

A Fully Coupled Sensitivity Analysis Framework for Offshore Wind Turbines Based on an XGBoost Surrogate Model and the Interpretation of SHAP

Jianfeng Xu, Sen Gong, Zhongbo Hu, Liangxian Li et al.
Sustainability
Wind Energy Research and Development
article

A Fully Coupled Sensitivity Analysis Framework for Offshore Wind Turbines Based on an XGBoost Surrogate Model and the Interpretation of SHAP

Jianfeng Xu, Sen Gong, Zhongbo Hu, Liangxian Li, Xin Li, Xinyi Liu, Wenhua Wang, Xiang Gao, Wei Vivien Shi
article en

Abstract

To advance global sustainability and meet climate targets, the development of reliable renewable energy infrastructure is paramount. Offshore wind energy is a key factor in achieving this goal, and ensuring its operational efficiency requires a deep understanding of the sources of uncertainty faced by offshore wind turbines (OWTs). This study proposes and implements an integrated framework for sensitivity analysis (SA) to investigate the key sources of uncertainty influencing the dynamic response of an OWT. This framework is based on the XGBoost surrogate model and Sobol’s method, aiming to efficiently and accurately quantify the impact of various uncertain parameters. A key methodological novelty lies in the integrated use of Sobol’s method and SHapley Additive exPlanations (SHAP), which provides a unique cross-validating mechanism for the sensitivity results. This study demonstrates the strongly condition-dependent nature of the OWT’s sensitivity characteristics by analyzing design load cases. The results indicate that wind speed is the dominant factor influencing the structural response under normal operating conditions. In contrast, under extreme shutdown conditions, the response of the OWT is primarily governed by the physical and material properties of the structure. In addition, the high consistency between the results of SHAP technology and the SA results obtained by Sobol’s method confirms the reliability of the proposed framework. The identified key sources of uncertainty provide direct practical insights for design optimization and reliability assessment of OWTs.

SustainabilityVol. 17(20)
Dalian University of Technology (CN), PowerChina (China) (CN)
Openalex Percentile: Top 61%
Wind Energy Research and Development
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