海上风力机响应极值预报研究进展综述

柴威; 何林; 施伟; 陈威; 曾佳焱; 杨清泉

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

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太阳能学报 ›› 2026, Vol. 47 ›› Issue (3) : 308-315. DOI: 10.19912/j.0254-0096.tynxb.2024-1950

海上风力机响应极值预报研究进展综述

柴威^1,2, 何林^1,2, 施伟³, 陈威^1,2, 曾佳焱², 杨清泉²

作者信息 +

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

Author information +

文章历史 +

摘要

综述海上风力机响应极值预报的研究进展,重点介绍基于统计分析和机器学习的极值预报方法。统计方法包括渐进极值法、阈值超越法、平均穿越率法和平均条件超越率法。统计方法通过样本数据建模风力机响应的概率分布模型,进行极值预报。基于机器学习的极值预报方法（如人工神经网络和高斯过程回归等算法）,通过提取数据特征并构建非线性模型,进行海上风力机响应极值预报,最后对各类极值预报方法的优缺点及适用场景进行总结,并展望极值预报技术的未来发展方向。

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.

导出引用

柴威, 何林, 施伟, 陈威, 曾佳焱, 杨清泉. 海上风力机响应极值预报研究进展综述[J]. 太阳能学报. 2026, 47(3): 308-315 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1950

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

中图分类号： TK89

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