风电机组叶片无损检测技术研究与进展

doi:10.11930/j.issn.1004-9649.202303073

摘要/Abstract

摘要：

风电机组叶片在运行时除了承受气动力作用外，还承受重力、离心力等其他力的影响，再加上雨雪、沙尘、盐雾侵蚀、雷击等破坏，使叶片基体及表面容易受到损伤，这些损伤如未及时发现与维修会导致风电机组发电效率下降、停机，甚至发生损毁等事故。因此，风电机组叶片损伤检测对保障风电机组安全高效运行、降低风电机组寿命周期内发电成本有重大意义。结合国内外相关文献，综述了风电机组叶片损伤类型及产生原因，对现有风电机组叶片损伤检测技术进行了系统介绍，对这些技术进行了实时在线监测与非实时检测分类，对比了各监测/检测技术的优缺点。最后根据风电机组的实际工程应用及无损检测技术的发展，提出了风电机组叶片无损监测/检测技术未来发展趋势。

关键词: 风电机组, 风电叶片, 无损检测, 应变, 振动

Abstract:

In addition to withstanding aerodynamic forces, blades also experience other forces such as gravity and centrifugal force during operation, as well as damage from rain, snow, sand, salt spray, lightning and other factors, making the wind turbine blade structure and surface vulnerable. Failure to detect and repair such damage in a timely manner can lead to reduced power generation efficiency, downtime and even accidents. Therefore, damage detection of wind turbine blades is of great significance for ensuring the safe and efficient operation of wind turbines and reducing the cost of power generation over the life cycle of the system. This article provides a comprehensive review of the types and causes of wind turbine blade damage based on relevant literature from both Chinese and international sources. It also systematically introduces existing wind turbine blade damage detection technologies, categorizing them into real-time online monitoring and non-real-time detection, and compares the advantages and disadvantages of each technology. Finally, based on the actual engineering application of wind turbines and the development of non-destructive testing technology, the future development trend of non-destructive monitoring/detection technology for wind turbine blades is proposed.

Key words: wind turbine, wind turbine blade, non-destructive testing, strain, vibration

王磊, 柳亦兵, 滕伟, 黄心伟, 刘剑韬. 风电机组叶片无损检测技术研究与进展[J]. 中国电力, 2023, 56(10): 80-95.

Lei WANG, Yibing LIU, Wei TENG, Xinwei HUANG, Jiantao LIU. Research and Development of Nondestructive Detection Technology for Wind Turbine Blades[J]. Electric Power, 2023, 56(10): 80-95.

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链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202303073

https://www.electricpower.com.cn/CN/Y2023/V56/I10/80

图/表 21

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监测方法	优点	缺点	损伤识别	损伤评估	损伤定位
应变监测	可以进行早期微小损伤监测可以监测内部损伤（FGB）受外部环境干扰小（FGB）长距离传输信号衰减小FGB）	为接触式监测需把传感器粘贴在叶片表面或嵌入到叶片内部需要提前预判风电叶片的高应变区域应变片传感器数量大走线复杂应变片传感器长时间使用会蠕变退化 FGB传感器需要嵌入叶片内部加了叶片制造难度	是	是	是
振动监测	应用广泛技术成熟传感器安装方便振动信号处理技术成熟多样	对前期微小损伤不敏感外界环境对振动信号影响大损伤特征提取困难需要传感器数量较多	是	可能	可能
声发射监测	对早期微小裂纹损伤敏感能持续监测损伤扩展不需要外部激励主动监测	信号受环境干扰大难以提取有效信号需要紧贴叶片安装以提高监测精度需提前判定损伤可能发生出需要多个声发射传感器，走线复杂	是	是	是
噪声监测	传感器安装方便成本低对早期损伤敏感麦克风阵列监测可以进行损伤定位	噪声信号受环境影响较大信号采样率高数据处理难度大对于持续的损伤监测不敏感	是	可能	可能
SCADA数据监测	不需要额外增加传感器成本低数据处理算法丰富	数据量庞大，提取有效数据困难对早期损伤不敏感，事后预警	是	否	否
基于数据融合的损伤监测	提高损伤检测的准确性监测范围广，可以检测叶片表面及内部损伤降低误报率	数据处理负载度高算法优化难度大	是	是	是

监测方法	优点	缺点	损伤识别	损伤评估	损伤定位
应变监测	可以进行早期微小损伤监测可以监测内部损伤（FGB）受外部环境干扰小（FGB）长距离传输信号衰减小FGB）	为接触式监测需把传感器粘贴在叶片表面或嵌入到叶片内部需要提前预判风电叶片的高应变区域应变片传感器数量大走线复杂应变片传感器长时间使用会蠕变退化 FGB传感器需要嵌入叶片内部加了叶片制造难度	是	是	是
振动监测	应用广泛技术成熟传感器安装方便振动信号处理技术成熟多样	对前期微小损伤不敏感外界环境对振动信号影响大损伤特征提取困难需要传感器数量较多	是	可能	可能
声发射监测	对早期微小裂纹损伤敏感能持续监测损伤扩展不需要外部激励主动监测	信号受环境干扰大难以提取有效信号需要紧贴叶片安装以提高监测精度需提前判定损伤可能发生出需要多个声发射传感器，走线复杂	是	是	是
噪声监测	传感器安装方便成本低对早期损伤敏感麦克风阵列监测可以进行损伤定位	噪声信号受环境影响较大信号采样率高数据处理难度大对于持续的损伤监测不敏感	是	可能	可能
SCADA数据监测	不需要额外增加传感器成本低数据处理算法丰富	数据量庞大，提取有效数据困难对早期损伤不敏感，事后预警	是	否	否
基于数据融合的损伤监测	提高损伤检测的准确性监测范围广，可以检测叶片表面及内部损伤降低误报率	数据处理负载度高算法优化难度大	是	是	是

检测方法	优点	缺点	损伤识别	损伤评估	损伤定位
红外热成像检测	可快速对风电叶片进行大面积损伤检测可对风电叶片内部损伤进行检测在无人机的配合下检测方便灵活	受外部环境如温度、湿度影响较大需要增加额外的热源需要进行大量的图像处理对早期微小损伤不敏感	是	是	是
机器视觉检测	方便高效可快速对风电叶片表面进行检测图像处理算法多可以进行实时图像处理	只能对风电叶片表面损伤进行检测受环境天气等因素影响大图像处理需要进行大量运算需要无人机进行紧密配合	是	是	是

检测方法	优点	缺点	损伤识别	损伤评估	损伤定位
红外热成像检测	可快速对风电叶片进行大面积损伤检测可对风电叶片内部损伤进行检测在无人机的配合下检测方便灵活	受外部环境如温度、湿度影响较大需要增加额外的热源需要进行大量的图像处理对早期微小损伤不敏感	是	是	是
机器视觉检测	方便高效可快速对风电叶片表面进行检测图像处理算法多可以进行实时图像处理	只能对风电叶片表面损伤进行检测受环境天气等因素影响大图像处理需要进行大量运算需要无人机进行紧密配合	是	是	是

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