In vibration testing of civil engineering structures, the first two vibration modes are crucial in representing the global dynamic behavior of the structure measured. In the present study, a comprehensive method is proposed to identify the first two vibration modes of wind turbine towers, which is based on the analysis of fractional order statistical moments (FSM). This study offers novel contributions in two key aspects: (1) theoretical derivations of the relationship between FSM and vibration mode; and (2) successful use of 32/7‐order displacement statistical moment as the optimal FSM to identify wind turbine tower modes, by combining with noise resistance analysis, sensitivity analysis, and stability analysis, respectively. Using the proposed method, the FSM was first used to identify the modal vibration of wind turbine towers. By obtaining the response of the structure on the same vertical line, FSM was then calculated to estimate the corresponding structural modal vibration. Considering other influencing factors in the field test, the modal identification results of this index under different excitation forms and noise conditions were analyzed based on numerical simulation and verified with field wind tower test data. The results of the evaluation show that the proposed statistical moments of can accurately identify the first two vibration modes of wind turbine towers. This presents a new robust method for modal vibration identification, that is, simple and effective in its implementation.

中文翻译：

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基于最优分数阶统计矩的风电机组塔筒模态辨识


在土木工程结构的振动测试中，前两种振动模式对于表示被测结构的整体动态行为至关重要。本文基于分数阶统计矩 （FSM） 分析，提出了一种识别风力发电机塔筒前两种振动模式的综合方法。本研究在两个关键方面做出了新的贡献：（1） FSM 与振动模式之间关系的理论推导;（2） 通过分别结合抗噪声分析、敏感性分析和稳定性分析，成功使用 32/7 阶位移统计矩作为最佳 FSM 来识别风力涡轮机塔筒模式。使用所提出的方法，FSM 首先用于识别风力涡轮机塔架的模态振动。通过获得结构在同一垂直线上的响应，然后计算 FSM 以估计相应的结构模态振动。考虑现场试验中的其他影响因素，基于数值模拟分析了该指标在不同激励形式和噪声条件下的模态识别结果，并用现场风塔试验数据进行了验证。评估结果表明，所提出的统计矩可以准确识别风电机组塔筒的前两种振动模式。这为模态振动识别提供了一种新的稳健方法，即实现简单有效。