基于改进FFT-小波变换的XLPE电力电缆局放降噪方法
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摘要
局部放电信号检测对于电力电缆绝缘状态评估具有重要的意义,但信号极其微弱,容易湮没在强烈的外部干扰之中。针对传统小波变换存在去噪效果差、信噪比不高的问题,提出一种快速傅里叶变换与小波变换结合的去噪算法。首先采用改进模糊C均值聚类阈值法对快速傅里叶变换去噪算法进行改进。改进模糊C均值聚类算法有着更优的初始聚类中心,聚类结果更容易收敛,使快速傅立叶变换去噪算法中干扰峰定位更加准确。然后利用改进的快速傅立叶变换去噪算法对周期性窄带噪声进行针对性处理,再结合小波变换方法,去除白噪声为主的剩余噪声,从而实现对局部放电信号的综合去噪。仿真结果表明,基于改进快速傅里叶变换-小波变换的去噪算法信噪比高,波形畸变小,去噪效果优于小波算法。
Partial discharge signal detection is of great importance for assessment of the insulation status of power cables,but the extremely weak signal is easily buried in strong external disturbance. A new de-noising algorithm,combining fast Fourier transform and wavelet transform,was proposed to solve the problem of poor de-noising effect and low signal-to-noise ratio of the traditional wavelet transform. Firstly,the improved fuzzy C-means clustering threshold method was used to improve the fast Fourier transform de-noising algorithm. The improved fuzzy C-means clustering algorithm had a better initial clustering center,and the clustering results were more likely to converge,thus achieving more accurate interference peak localization in the fast Fourier transform de-noising algorithm. Then,the improved fast Fourier transform de-noising algorithm was used to deal with the periodic narrowband noise. In combination with the wavelet transform method,residual noise mainly composed of white noise was removed to realize comprehensive de-noising of the partial discharge signal. Simulation results showed that the de-noising algorithm based on improved fast Fourier transform-wavelet transform had a high signal-to-noise ratio and small waveform distortion,and could produce a better de-noising effect than wavelet algorithm.
Partial discharge signal detection is of great importance for assessment of the insulation status of power cables,but the extremely weak signal is easily buried in strong external disturbance. A new de-noising algorithm,combining fast Fourier transform and wavelet transform,was proposed to solve the problem of poor de-noising effect and low signal-to-noise ratio of the traditional wavelet transform. Firstly,the improved fuzzy C-means clustering threshold method was used to improve the fast Fourier transform de-noising algorithm. The improved fuzzy C-means clustering algorithm had a better initial clustering center,and the clustering results were more likely to converge,thus achieving more accurate interference peak localization in the fast Fourier transform de-noising algorithm. Then,the improved fast Fourier transform de-noising algorithm was used to deal with the periodic narrowband noise. In combination with the wavelet transform method,residual noise mainly composed of white noise was removed to realize comprehensive de-noising of the partial discharge signal. Simulation results showed that the de-noising algorithm based on improved fast Fourier transform-wavelet transform had a high signal-to-noise ratio and small waveform distortion,and could produce a better de-noising effect than wavelet algorithm.
引文
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[10]罗新,牛海清,胡日亮,等.一种改进的用于快速傅里叶变换功率谱中的窄带干扰抑制的方法[J].中国电机工程学报,2013,50(12):167-175,200.
[11]唐成华,刘鹏程,汤申生,等.基于特征选择的模糊聚类异常入侵行为检测[J].计算机研究与发展,2015,52(3):718-728.
[2]王恩俊,张建文,马晓伟,等.基于CEEMD-EEMD的局部放电阈值去噪新方法[J].电力系统保护与控制,2016,44(15):93-98.
[3]LIN X,HUANG J,XU Y,et al.A wavelet de-nosing algorithm of XLPE cable partial discharge signals based on chaotic simulated annealing[C]//International Conference on Electronics Information and Emergency Communication.IEEE,2015:333-336.
[4]ZHOU R,BAO W,LI N,et al.Mechanical equipment fault diagnosis based on redundant second generation wavelet packet transform[J].Digital Signal Processing,2010,20(1):276-288.
[5]王永强,谢军,律方成.基于改进量子粒子群优化稀疏分解的局放信号去噪方法[J].电工技术学报,2015,30(12):320-329.
[6]钱勇,黄成军,陈陈,等.多小波消噪算法在局部放电检测中的应用[J].中国电机工程学报,2007,44(6):89-95.
[7]郭灿新,勇明,徐敏骅,等.S变换在电力电缆局部放电信号时频分析中的应用[J].电工技术学报,2010,25(11):9-14.
[8]罗新,牛海清,胡日亮,等.基于小波包分解的XLPE配电电缆局部放电波形特征提取与识别[J].高压电器,2013,56(11):110-116,122.
[9]唐明,梁得亮,王青山,等.矿用电缆局部放电监测的最优二代小波包基降噪[J].西安交通大学学报,2013,54(12):32-37,76.
[10]罗新,牛海清,胡日亮,等.一种改进的用于快速傅里叶变换功率谱中的窄带干扰抑制的方法[J].中国电机工程学报,2013,50(12):167-175,200.
[11]唐成华,刘鹏程,汤申生,等.基于特征选择的模糊聚类异常入侵行为检测[J].计算机研究与发展,2015,52(3):718-728.