基于傅里叶分解与排列熵的水轮机组去噪方法研究

Denoising Algorithm of Hydro-generator Set Based on Fourier Decomposition and Permutation Entropy

针对强背景噪声下水轮发电机组原始信号难以提取的问题,提出了基于傅里叶分解(FDM)和排列熵相结合的水轮发电机组背景噪声去除方法。首先基于傅里叶分解理论将水轮发电机组信号分解为若干个瞬时频率的固有频带函数;然后对其进行排列熵的计算,利用排列熵对噪声的敏感特性,对所得的固有频带函数进行筛选,将符合条件的函数进行重构,达到对信号去噪目的;最后利用该方法进行仿真和实例分析,同时与EMD排列熵方法进行对比。结果表明,基于FDM与排列熵方法去噪效果更好,更适合应用于水轮发电机组背景噪声的去除。

In order to solve the problem that it is difficult to extract the original real signal of hydro-generator set under strong background noise,a background noise removal method based on the combination of Fourier decomposition method(FDM)and permutation entropy is proposed.First,based on Fourier's decomposition theory,the noisy signal of a hydro-generator set is decomposed into several natural frequency band functions of instantaneous frequencies.Then the permutation entropy is calculated so as to select the natural frequency band function,and the qualified functions are reconstructed to achieve the goal of signal de-noising.Finally,the simulation and example analysis are carried out by using the method,and the results are compared with EMD permutation entropy.The results show that the FDM and permutation entropy are better methods for noise reduction,and they are more suitable for the background noise reduction of hydro-generator set.