风电叶片表面缺陷损伤风险判定方法

王俭; 方健豪; 王金袖; 赵一锋; 胡伟飞; 范佳

doi:10.19912/j.0254-0096.tynxb.2024-2333

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风电叶片表面缺陷损伤风险判定方法

更新时间：2026-07-21

- 风电叶片表面缺陷损伤风险判定方法
- Vol. 47, Issue 5, Pages: 258-267(2026)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：10.19912/j.0254-0096.tynxb.2024-2333
  CLC： TP391
- Online First：04 June 2026，
  
  Published：2026
- 稿件说明：
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王俭, 方健豪, 王金袖, et al. 风电叶片表面缺陷损伤风险判定方法[J]. 2026, 47(5): 258-267.
DOI：

王俭, 方健豪, 王金袖, et al. 风电叶片表面缺陷损伤风险判定方法[J]. 2026, 47(5): 258-267. DOI： 10.19912/j.0254-0096.tynxb.2024-2333.

摘要

提出一种用于评估和量化风电叶片表面缺陷损伤风险的方案。该方法通过图像处理和分析技术

不仅能检测出风力机表面的缺陷

还能准确评估其对风力机运行的潜在影响

为风力机运维提供了一个有效的工具

帮助运维人员更好地理解和管理风力机表面的健康状况。构建一个实地采集的风电叶片表面缺陷图像数据集

以验证该方法的可行性。实验结果显示

对于裂缝缺陷

测试集的量化结果误差范围为0.01~0.21 m（0.43%~23.81%）

平均相对误差为7.24%。对于叶片前缘侵蚀

误差范围为0.00192~0.02940 m2（1.27%~30.18%）

平均相对误差为13.06%。对于油漆剥落

误差范围为0.00320~0.01729 m2（0.65%~22.81%）

平均相对误差为9.44%。结果表明

所提方法在实际应用中有效。

Abstract

This paper proposes a novel methodology to assess and quantify the risk of surface defects on wind turbine blades. Leveraging advanced image processing and analysis techniques

the approach not only detects surface defects but also accurately evaluates their potential impacts on wind turbine operation

providing an effective tool for condition-based maintenance. A field-collected dataset of wind turbine blade surface defect images was constructed in this paper to verify the feasibility of this method. Experimental results show that for crack defects

the error range in the test set was 0.01 m to 0.21 m （0.43% to 23.81%）

with an average relative error of 7.24%. For the blade leading edge erosion

the error range was 0.00192 m to 0.0294 m （1.27% to 30.18%）

with an average relative error of 13.06%. For the paint peeling

the error range was 0.00320 m to 0.01729 m （0.65% to 22.81%）

with an average relative error of 9.44%. These results indicate that the proposed method is effective for practical applications.

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