模态参数是体现海上风电结构运行安全状态的关键指标，然而复杂多变的海洋环境会导致实测振动信号中混有大量低频高能噪声，严重影响模态识别精度。为实现海上风电结构模态参数的准确识别，本文提出一种能够抵抗低频高能噪声影响的模态参数识别方法（CVS）。首先利用完全自适应噪声集合经验模态分解法（complementary ensemble empirical mode decomposition with adaptive noise，CEEMDAN）滤除原始信号中的高频噪声，随后通过麻雀优化算法（sparrow's optimization algorithm，SSA）以最小包络熵作为适应度函数迭代计算自适应确定变分模态分解法（variational mode decomposition，VMD）的信号分解层数K和惩罚因子 ，实现信号的VMD自适应优化分解以剔除低频高能噪声影响，最后再采用随机子空间方法（SSI）实现信号中模态参数的识别提取。研究分别针对构造仿真含噪信号和原型观测信号开展了识别效果对比验证，结果表明，相比于传统模态识别方法，CVS方法在信噪比、波形相似系数、相对误差等参数方面具有更好的有效性和精确性。同时，该方法对实测信号的处理能力强，降噪效果好，能够准确识别结构固有频率、叶轮转动频率（1P）和叶片扫掠频率（3P），具有良好的工程适用性，为后续基于实测数据开展海上风电结构模态参数识别与运行安全评价提供了新思路。
Abstract
Modal parameters are the key indexes to reflect the safe operation of offshore wind turbine (OWT) structures. However, the complex and changeable ocean environment will lead to a large amount of low-frequency and high-energy noise mixed in the measured vibration signals, which seriously affects the accuracy of modal identification. To achieve the accurate identification of modal parameters of OWT structures, one modal parameter identification method (CVS) that can resist the influence of low-frequency high-energy noise was proposed in this research. Firstly, the method filters out the high-frequency noise in the original signal using the Complementary Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN). Then the Sparrow's Optimization Algorithm (SSA) is utilized to iteratively calculate the number of signal decomposition layers K and the penalty factor α of the adaptive deterministic variational modal decomposition (VMD) by the minimum envelope entropy as the fitness function, achieving VMD adaptive optimization decomposition of the signal to eliminate the influence of low-frequency high-energy noise. Finally, the stochastic subspace identification (SSI) method is used to identify and extract modal parameters from the signal. The study was carried out to verify the identification effect of the constructive simulation noise-containing signals and the prototype observation signals, respectively. The results show that the CVS method has better effectiveness and accuracy in parameters such as signal-to-noise ratio, waveform similarity coefficient, and relative error compared with the traditional modal identification method. Meanwhile, the method has strong processing capability and good noise reduction effect on the measured signals, and can accurately identify the structural natural frequency, impeller rotation frequency (1P) and blade sweep frequency (3P). It has good engineering applicability and can provide a new idea for identifying the modal parameters of OWT structures based on measured data in the future.
关键词
海上风电 /
模态参数识别 /
低频高能噪声 /
完全自适应噪声集合经验模态分解（CEEMDAN） /
变分模态分解法（VMD）
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Key words
Offshore wind power /
Modal parameter identification /
Low-frequency high-energy noise /
Complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) /
Variational modal decomposition (VMD)
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