EN风力机结构的实时疲劳损伤预测方法
风力机结构的实时疲劳损伤预测方法
- 太阳能学报 2025年第10期 页码：655-662
- 作者机构：
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DOI：doi:10.19912/j.0254-0096.tynxb.2024-1010
中图分类号： K83-
网络出版：2025-10-31，
纸质出版：2025-10-31
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赵艳, 孙硕程, 王新武, 等. 风力机结构的实时疲劳损伤预测方法[J]. 太阳能学报, 2025,(10):655-662.
赵艳, 孙硕程, 王新武, et al. 风力机结构的实时疲劳损伤预测方法[J]. 2025, (10): 655-662.
赵艳, 孙硕程, 王新武, 等. 风力机结构的实时疲劳损伤预测方法[J]. 太阳能学报, 2025,(10):655-662. DOI： doi:10.19912/j.0254-0096.tynxb.2024-1010.
赵艳, 孙硕程, 王新武, et al. 风力机结构的实时疲劳损伤预测方法[J]. 2025, (10): 655-662. DOI： doi:10.19912/j.0254-0096.tynxb.2024-1010.
摘要
为掌握风力机塔架实际消耗的疲劳损伤
基于振动监测对风力机进行疲劳评估至关重要。对1.5 MW风力机的塔底应变进行长期监测
基于雨流计数法提出实时预测疲劳损伤的新方法。该方法通过将过去无法形成完整循环的残波和新测量的应力数据串联
预测当前的损伤
并将新的残波储存
继续和后续测量应力结合
实时计算风力机的疲劳损伤。使用该方法得到的疲劳损伤和将已知数据作为连续序列得到的结果完全相同
证明了该方法的准确性。同时
该方法只需储存历史数据的残波即可
占用的内存更少
这为风力机结构疲劳损伤的在线实时预测提供了解决方案。
Abstract
In order to understand the actual fatigue damage and remaining service life of wind turbines
it is crucial to conduct fatigue assessment of wind turbines based on vibration monitoring. Through long-term monitoring of tower bottom strain of a 1.5MW wind turbine
a new method for real-time prediction of fatigue damage was proposed based on the traditional four-point rainflow cycle counting algorithm. Traditional methods only considered stress cycles within subsets and ignored transition cycles between subsets. The new method proposed in this article predicted the current fatigue damage by concatenating residue series that could not form complete cycles in the past stresses with newly measured stresses. The new residue series were then stored and combined with subsequent measured stresses to evaluate the fatigue damage of wind turbines in real time. The fatigue damage obtained by using this method was identical to the results obtained by using known data as a continuous sequence
proving the accuracy of this method. At the same time
in this method only the residue series of historical data need to stored
which occupy less memory
this article also proveds that the accuracy of this method is not affected by the calculation window length. This provides a solution for online real-time prediction of fatigue damage in wind turbine structures.
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references
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