田毓龙, 郑祥豪, 李浩等. 基于集合经验模态分解和指数能量法的水泵水轮机尾水管压力脉动信号特征提取. 力学与实践, 2024, 46(2): 1-8. doi: 10.6052/1000-0879-23-032
引用本文: 田毓龙, 郑祥豪, 李浩等. 基于集合经验模态分解和指数能量法的水泵水轮机尾水管压力脉动信号特征提取. 力学与实践, 2024, 46(2): 1-8. doi: 10.6052/1000-0879-23-032
Tian Yulong, Zheng Xianghao, Li Hao, et al. Feature extraction of pressure pulsation signal in draft tube of a pump turbine based on ensemble empirical mode decomposition and index energy. Mechanics in Engineering, 2024, 46(2): 1-8. doi: 10.6052/1000-0879-23-032
Citation: Tian Yulong, Zheng Xianghao, Li Hao, et al. Feature extraction of pressure pulsation signal in draft tube of a pump turbine based on ensemble empirical mode decomposition and index energy. Mechanics in Engineering, 2024, 46(2): 1-8. doi: 10.6052/1000-0879-23-032

基于集合经验模态分解和指数能量法的水泵水轮机尾水管压力脉动信号特征提取

FEATURE EXTRACTION OF PRESSURE PULSATION SIGNAL IN DRAFT TUBE OF A PUMP TURBINE BASED ON ENSEMBLE EMPIRICAL MODE DECOMPOSITION AND INDEX ENERGY

  • 摘要: 提取水泵水轮机尾水管压力脉动信号中的动态特征信息,准确识别涡带强度,是近年来水泵水轮机工程领域的研究重点。本文基于集合经验模态分解(ensemble empirical mode decomposition, EEMD)和模态指数能量法,对某水泵水轮机发电工况不同负荷下的尾水管压力脉动信号进行特征提取,得到如下结论。首先,基于EEMD的模态指数能量能够有效地反映信号中的能量分布规律。其次,在涡带增强过程中,基于EEMD的最大模态指数能量不断升高,表明尾水管内的流动状况变得更加复杂,涡带特征信息也更加丰富。最后,使用最大与平均指数能量构建的特征向量能够准确反映不同的尾水管涡带强度,并且能够作为智能分类器的输入特征向量,有利于后续进一步的识别与诊断,具有重要的工程意义。

     

    Abstract: Extracting the dynamic feature information of the pressure pulsation signal in the draft tube of the pump turbine and accurately identifying the strengths of vortex rope are the research focuses in the engineering field of the pump turbine in recent years. Based on ensemble empirical mode decomposition (EEMD) and index energy of mode, the features of the pressure pulsation signals in the draft tube of a pump turbine at different load conditions in the generating mode are extracted in the present paper, and the following conclusions are obtained. Firstly, the index energies of modes based on EEMD can effectively reflect the energy distribution in the signal. Secondly, in the process of the increment of the strength of vortex rope, the maximum index energy of mode based on EEMD increases continuously, indicating that the flow status in the draft tube becomes more complex and the feature information of vortex rope is also more abundant. Finally, the eigenvector constructed using the maximum and mean index energies can accurately reflect different strengths of vortex ropes in the draft tube. And it can be adopted as the input eigenvector of the intelligent classifier, which is conducive to further recognition and diagnosis, and has important engineering significance.

     

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