基于机器学习的风电叶片关键部位设计和优化方法

doi:10.19936/j.cnki.2096-8000.20260128.017

复合材料科学与工程 ›› 2026, Vol. 0 ›› Issue (1): 124-132.DOI: 10.19936/j.cnki.2096-8000.20260128.017

基于机器学习的风电叶片关键部位设计和优化方法

刘俊邦^1,2, 刘清¹, 林启扬², 张文华², 黄轩晴^1*

1.明阳智慧能源集团股份公司,中山 528400;
2.暨南大学力学与建筑工程学院,广州 510000

收稿日期:2024-10-28 出版日期:2026-01-28 发布日期:2026-03-12
通讯作者: 黄轩晴(1991—),男,硕士,中级工程师,主要从事风力发电机组设计方面的工作,huangxuanqing@mywind.com.cn。
作者简介:刘俊邦(1997—),男,硕士,中级工程师,主要从事风电叶片结构性能方面的工作。
基金资助:
内蒙古自治区“双碳”科技创新重大示范工程“揭榜挂帅”项目(2022JBGS0045)

Machine learning-based design and optimization method of critical parts of wind turbine blades

LIU Junbang^1,2, LIU Qing¹, LIN Qiyang², ZHANG Wenhua², HUANG Xuanqing^1*

1. Ming Yang Smart Energy Group Limited, Zhongshan 528400, China;
2. The School of Mechanism and Construction Engineering, Jinan University, Guangzhou 510000, China

Received:2024-10-28 Online:2026-01-28 Published:2026-03-12

摘要/Abstract

摘要： 对风电叶片的主梁结构性能进行分析,结合FOCUS和Python语言开发了一套基于机器学习模型的风电叶片关键部件逆向结构优化方法。以某1.5 MW风电叶片设计为例,建立了叶片有限元分析模型。以主梁铺层厚度为设计变量,以主梁应变峰值为优化目标,通过建立机器学习模型来反映主梁铺层参数和应变及质量的底层映射关系,并基于此机器学习模型开发了一种基于机器学习的自学习循环优化方法。实现了同一型号叶片的关键部位在不同风场、不同工况载荷下的快速迭代。优化后的主梁保持成本不变,性能提升约11.44%。该方法以其高度可移植性的特点有望成为叶片各关键部位设计与优化的有效工具。

关键词: 风电叶片, 主梁应变, 有限元计算, 机器学习, 逆向优化, 复合材料

Abstract: An analysis of the structural performance of the spar cap of wind turbine blades was conducted, and a reverse structural optimization method for key components of wind turbine blades based on a machine learning model was developed, combining the use of FOCUS and the Python programming language. Taking a 1.5 MW wind turbine blade design as an example, the finite element analysis (FEA) model of the blade was established. The thickness of the spar cap was selected as the design variable, and the peak strain of the spar cap served as the optimization objective. A machine learning model was developed to reflect the underlying mapping relationship between the spar cap layup parameters, strain, and mass. Based on this machine learning model, a self-learning cyclic optimization method was developed. This method enables the rapid iteration of key parts of the same blade type under different wind fields and load conditions. The optimized spar cap improves performance by about 11.44% while maintaining the same cost. Due to its high portability, this method is expected to become an effective tool for the design and optimization of key parts of wind turbine blades.

Key words: wind turbine blades, spar cap strain, FEM, machine learning, reverse optimization, composites

中图分类号:

TB332

刘俊邦, 刘清, 林启扬, 张文华, 黄轩晴. 基于机器学习的风电叶片关键部位设计和优化方法[J]. 复合材料科学与工程, 2026, 0(1): 124-132.

LIU Junbang, LIU Qing, LIN Qiyang, ZHANG Wenhua, HUANG Xuanqing. Machine learning-based design and optimization method of critical parts of wind turbine blades[J]. COMPOSITES SCIENCE AND ENGINEERING, 2026, 0(1): 124-132.

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基于机器学习的风电叶片关键部位设计和优化方法

Machine learning-based design and optimization method of critical parts of wind turbine blades

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