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水力发电学报 ›› 2017, Vol. 36 ›› Issue (8): 78-85.doi: 10.11660/slfdxb.20170809

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基于VMD和排列熵的水轮机压力脉动信号去噪算法

  

  • 出版日期:2017-08-25 发布日期:2017-08-25

Denoising algorithm of pressure fluctuation signals of hydraulic turbines based on VMD and permutation entropy

  • Online:2017-08-25 Published:2017-08-25

摘要: 水轮机尾水管压力脉动信号十分复杂,在水电机组故障诊断中表现出强烈的不平稳性。基于此本文提出结合可变模态分解(Variational Mode Decomposition,VMD)和排列熵(Permutation Entropy,PE)的水轮机压力脉动信号去噪新方法。该方法首先将压力脉动信号进行VMD分解,获取若干BIMF分量,求取各模态分量的排列熵,利用排列熵对信号随机性的敏感特性,对压力脉动信号筛选并重构,完成对信号的去噪。对比仿真和实例结果分析,该方法优于目前的EEMD滤波算法,能有效去除噪声,具有良好的去噪效果,为水电机组故障特征提取提供了新思路。

Abstract: Turbine draft tube pressure fluctuation signals are very complicated and show a strong instability in fault diagnosis of hydroelectric generating units. This paper presents a new method for denoising pressure fluctuation signals of hydraulic turbines based on variational mode decomposition (VMD) and permutation entropy (PE). In this method, a pressure fluctuation signal is decomposed by VMD, and a number of its BIMF components are obtained. Then, the permutation entropy of each modal component is calculated, and the pressure fluctuation signal is screened and reconstructed by using these permutation entropies’ sensitivity to the feature of random signals. Comparison of simulation results shows that our new method can effectively denoise the signals and is better than the commonly-used EEMD filtering algorithm.

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