风电叶片摆锤式疲劳加载特性分析及研究

周爱国, 董涛, 施金磊, 朱玉田, 孙元荣, 翁振辉

太阳能学报 ›› 2024, Vol. 45 ›› Issue (12) : 214-219.

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太阳能学报 ›› 2024, Vol. 45 ›› Issue (12) : 214-219. DOI: 10.19912/j.0254-0096.tynxb.2023-1166

风电叶片摆锤式疲劳加载特性分析及研究

  • 周爱国1, 董涛1, 施金磊1, 朱玉田1, 孙元荣2, 翁振辉2
作者信息 +

ANALYSIS AND RESEARCH ON PENDULUM FATIGUE LOADING CHARACTERISTICS OF WIND TURBINE BLADES

  • Zhou Aiguo1, Dong Tao1, Shi Jinlei1, Zhu Yutian1, Sun Yuanrong2, Weng Zhenhui2
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摘要

为探究疲劳加载过程中摆锤式激振器相关参数对叶片振动特性的影响,揭示加载过程中激振器电流的变化规律,基于拉格朗日方程建立叶片摆锤式疲劳加载系统动力学模型,分析摆锤式激振器的动力参数以及与叶片的相互作用,并对叶片进行疲劳加载。结果表明,激振器中旋转质量会改变叶片疲劳加载系统的固有频率,采用实测加载数据中旋转质量与叶片振动之间的实际相位差,修正理论模型,理论电流峰值相对于实际电流误差为8.5%。

Abstract

To investigate the impact of relevant parameters of a rotating eccentric mass exciter on blade vibration characteristics during fatigue loading, as well as to further reveal the variation law of exciter current in actual test, a dynamic model of the rotating eccentric mass fatigue test system is established based on the Lagrange kinetic equation. The dynamic parameters of the rotating eccentric mass exciter and its interaction with the blade are analyzed. The results indicate that the rotating eccentric mass in the exciter will change the modal frequency of the blade fatigue loading system. Based on the actual phase difference between the rotating eccentric mass and the blade vibration in the real loading data, the theoretical model is modified. The current peak predicted by the improved theoretical model has an error of 8.5% relative to the actual current.

关键词

风电叶片 / 摆锤 / 疲劳测试 / 动力学分析 / 负载特性 / 频率响应

Key words

wind turbine blades / pendulums / fatigue testing / kinetic analysis / load characteristics / frequency response

引用本文

导出引用
周爱国, 董涛, 施金磊, 朱玉田, 孙元荣, 翁振辉. 风电叶片摆锤式疲劳加载特性分析及研究[J]. 太阳能学报. 2024, 45(12): 214-219 https://doi.org/10.19912/j.0254-0096.tynxb.2023-1166
Zhou Aiguo, Dong Tao, Shi Jinlei, Zhu Yutian, Sun Yuanrong, Weng Zhenhui. ANALYSIS AND RESEARCH ON PENDULUM FATIGUE LOADING CHARACTERISTICS OF WIND TURBINE BLADES[J]. Acta Energiae Solaris Sinica. 2024, 45(12): 214-219 https://doi.org/10.19912/j.0254-0096.tynxb.2023-1166
中图分类号: TH113    TK83   

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基金

工业和信息化部科技项目(2022-231-222)

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