REVIEW OF RESEARCH PROGRESS IN EXTREME VALUE PREDICTION FOR OFFSHORE WIND TURBINE RESPONSES

Chai Wei; He Lin; Shi Wei; Chen Wei; Zeng Jiayan; Yang Qingquan

doi:10.19912/j.0254-0096.tynxb.2024-1950

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Acta Energiae Solaris Sinica ›› 2026, Vol. 47 ›› Issue (3) : 308-315. DOI: 10.19912/j.0254-0096.tynxb.2024-1950

REVIEW OF RESEARCH PROGRESS IN EXTREME VALUE PREDICTION FOR OFFSHORE WIND TURBINE RESPONSES

Chai Wei^1,2, He Lin^1,2, Shi Wei³, Chen Wei^1,2, Zeng Jiayan², Yang Qingquan²

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Abstract

This paper reviews recent developments in short-term extreme value prediction for offshore wind turbine responses, with a particular focus on statistical and machine learning methods. Statistical methods, including the Gumbel distribution, Peak Over Threshold （POT）, Mean Upcrossing, and Average Conditional Exceedance Rate （ACER） methods, model turbine response probability distributions based on sample data for short-term extreme value estimation. Additionally, machine learning techniques, such as artificial neural networks and Gaussian process regression, improve prediction accuracy by automatically extracting relevant data features and constructing nonlinear models. This review highlights the strengths and limitations of various short-term extreme value prediction methods, discusses their applicable scenarios, and outlines potential future directions for advancing prediction technologies.

Key words

offshore wind turbines / responses / extreme value prediction / statistical methods / machine learning / surrogate model

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Chai Wei, He Lin, Shi Wei, Chen Wei, Zeng Jiayan, Yang Qingquan. REVIEW OF RESEARCH PROGRESS IN EXTREME VALUE PREDICTION FOR OFFSHORE WIND TURBINE RESPONSES[J]. Acta Energiae Solaris Sinica. 2026, 47(3): 308-315 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1950

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