基于小波分解与滑动峰态的微弱放电信号联合检测方法  

Weak partial discharge signal detection based on wavelet decomposition and sliding kurtosis

作　　者：刘卫东[1] 刘尚合[1] LIU Weidong LIU Shanghe(State Key Laboratory of Electromagnetic Environment Effect,Ordnance Engineering College, Shijiazhuang 050003,Chin)

机构地区：[1]军械工程学院电磁环境效应国家级重点实验室,河北石家庄050003

出　　处：《电力自动化设备》2016年第11期152-156,164,共6页Electric Power Automation Equipment

基　　金：国家自然科学基金资助项目(61172035)~~

摘　　要：基于四阶累积量的滑动峰态算法在表征微弱信号的非高斯特性方面具有优势,可用于对微弱放电信号进行检测。但在混合噪声干扰下,受固定滑动窗宽度的影响,该算法对微弱放电信号的检测性能会显著劣化。针对该问题,提出了基于小波分解和滑动峰态相结合的联合检测方法,并进行了仿真分析和实验验证,结果表明:利用小波变换对信号的多尺度分解以及信号在不同分解尺度上的特征差异,该联合检测方法实现了信号在多个优化的滑动窗宽度共同作用下的峰态检测,进一步增强了其抗噪声干扰能力,对微弱放电信号的检测性能相比原有滑动峰态算法有明显提升。For the detection of weak partial discharge signals,the sliding kurtosis algorithm based on the fourth-order cumulant is superior in presenting their non-gaussian characteristics,but its detection performance is obviously degraded due to the fixed sliding window width under the interference of mixed noises,aiming at which,a method of joint detection based on the wavelet decomposition and the sliding kurtosis is proposed and verified by simulation and experiment. Results show that,the proposed method decomposes the signals on multiple scales and takes advantage of the characteristic difference between the signals on different decomposition scales to realize the kurtosis detection with several optimal sliding window widths. With enhanced antinoise ability,the proposed method has better detection performance than the original sliding kurtosis method.

关 键 词：局部放电 非接触式检测 微弱信号 非高斯信号 小波分解 滑动峰态 

分 类 号：TM83[电气工程—高电压与绝缘技术] TP391.9[自动化与计算机技术—计算机应用技术]