用复小波包变换抑制局部放电检测中的窄带干扰研究
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摘要
气体绝缘组合电器(Gas Insulated Switchgear,简称GIS)内部产生局部放电(Partial Discharge,简称PD)是引发事故的重要因素之一,对其进行在线监测的难点主要是从强烈的电磁干扰环境中准确获取PD信号。目前PD信号检测中,尚无理想的抑制窄带干扰的技术和手段,因此本文提出了一个全新的抑制窄带干扰的技术即复小波包变换技术,给出了其构造方法和具体步骤,并用复小波包变换对仿真和实验室人工实测PD信号中的窄带干扰进行了去噪研究。
采用相同滤波器构造小波包和“保持实小波包滤波器的幅频特性,改变其相频特性”构造复小波的方法,构造了与相应实小波包具有相同幅频特性和与相应复小波具有相同相频特性的复小波包,给出了构造复小波包的具体方法,并用描述波形相似程度的NCC和VTP参数以及幅值相对误差,对原始PD仿真信号分解后直接重构的效果进行了定量评价,验证了其构造的正确性。
对4种常用PD仿真信号进行了复小波包分解与重构,并用波形相似程度的NCC和VTP参数以及幅值相对误差,对含有不同频率、不同强度窄带干扰的PD仿真信号波形进行了重构前后的相似程度分析,结果表明,复小波包变换对非平稳PD脉冲信号畸变小且还原信号的能力强。
根据PD信号与窄带干扰经复小波包变换后的不同特点,构造出抑制窄带干扰的去噪复合信息WTRIn序列,然后对比简单信息和几种其它复合信息对窄带干扰的抑止能力,证明了该复合信息序列有更强的去噪能力;最后研究了指数n对WTRIn去噪性能的影响,结果表明,在不同的窄带干扰频率和干扰强度下,复合信息WTRI的去噪效果总是最好。
最后,用复小波包变换技术对含有不同频率、不同强度窄带干扰的PD混合仿真和实测信号进行了去噪应用,解决了窄带干扰频率在检测仪器频带内用硬件滤波对局放信号有较大损失和用小波包变换去噪效果较差的难题。
Partial Discharge (PD) in Gas Insulated Switchgear (GIS) is one of the important factors for the fault; therefore the on-line monitoring PD in GIS has been researched at home and abroad in order to guarantee the safe operation of GIS. At the same time, PD pattern recognition is still an advanced problem to be studied, so this paper advance complex wavelets packet transform which is a bran-new technique to suppress narrow band noise, give its constructing methods and specific steps, and then research de-noising the simulative and measured signals in lab with narrow band noise by the complex wavelets packet transform.
By using the same filter to construct wavelet packet and by remaining phase spectrum of real wavelet packet filter and changing its amplitude-frequency characteristic, complex wavelets packet is constructed with the same amplitude-frequency characteristics of corresponding real wavelet packet and the same phase spectrum of corresponding complex wavelets, and then give the specific method of constructing complex wavelets packet, evaluate the effect of original PD simulative signals’direct reconstruction after decomposition by using Normalized Correlation Coefficient (NCC) and Variational Trend Parameter (VTP) also and the relative error of the amplitude, verify the capability of restore Non-stationary Oscillating Partial Discharge Signals effectively and the correctness of the complex wavelets packet’s construction.
Four common simulative signals are disassembled and constructed, the resemble degree analysis on simulative signals with different frequency and strength narrow band noises before and after construction, it is shown that complex wavelets packet transform suppress unsmooth pulse signals more effectively revert signals more powerful.
According to the differences between PD signals and the narrow band noise after complex wavelet packet transform, this paper presented the specific combined information of serial WTRIn, which was used to suppress narrow band noise. Through the contrat of suppressing narrow band niose, it was proven that WTRIn had more powerful than simple information and other combined-informations. At last, through the analysis of the impacts of the index n on WTRIn it was shown that WTRI is the-best-denoising-combined-information no matter what frequency and intensity of the narrow band noise.
At last, this paper denoised PD admixture simulative and measured signals with different frequency and strength noises by complex wavelets packet transform, overcomed the difficult of more loss using hardware filter and bad effect using wavelets packet transformation when narrow band noise in the monitoring frequency band.
采用相同滤波器构造小波包和“保持实小波包滤波器的幅频特性,改变其相频特性”构造复小波的方法,构造了与相应实小波包具有相同幅频特性和与相应复小波具有相同相频特性的复小波包,给出了构造复小波包的具体方法,并用描述波形相似程度的NCC和VTP参数以及幅值相对误差,对原始PD仿真信号分解后直接重构的效果进行了定量评价,验证了其构造的正确性。
对4种常用PD仿真信号进行了复小波包分解与重构,并用波形相似程度的NCC和VTP参数以及幅值相对误差,对含有不同频率、不同强度窄带干扰的PD仿真信号波形进行了重构前后的相似程度分析,结果表明,复小波包变换对非平稳PD脉冲信号畸变小且还原信号的能力强。
根据PD信号与窄带干扰经复小波包变换后的不同特点,构造出抑制窄带干扰的去噪复合信息WTRIn序列,然后对比简单信息和几种其它复合信息对窄带干扰的抑止能力,证明了该复合信息序列有更强的去噪能力;最后研究了指数n对WTRIn去噪性能的影响,结果表明,在不同的窄带干扰频率和干扰强度下,复合信息WTRI的去噪效果总是最好。
最后,用复小波包变换技术对含有不同频率、不同强度窄带干扰的PD混合仿真和实测信号进行了去噪应用,解决了窄带干扰频率在检测仪器频带内用硬件滤波对局放信号有较大损失和用小波包变换去噪效果较差的难题。
Partial Discharge (PD) in Gas Insulated Switchgear (GIS) is one of the important factors for the fault; therefore the on-line monitoring PD in GIS has been researched at home and abroad in order to guarantee the safe operation of GIS. At the same time, PD pattern recognition is still an advanced problem to be studied, so this paper advance complex wavelets packet transform which is a bran-new technique to suppress narrow band noise, give its constructing methods and specific steps, and then research de-noising the simulative and measured signals in lab with narrow band noise by the complex wavelets packet transform.
By using the same filter to construct wavelet packet and by remaining phase spectrum of real wavelet packet filter and changing its amplitude-frequency characteristic, complex wavelets packet is constructed with the same amplitude-frequency characteristics of corresponding real wavelet packet and the same phase spectrum of corresponding complex wavelets, and then give the specific method of constructing complex wavelets packet, evaluate the effect of original PD simulative signals’direct reconstruction after decomposition by using Normalized Correlation Coefficient (NCC) and Variational Trend Parameter (VTP) also and the relative error of the amplitude, verify the capability of restore Non-stationary Oscillating Partial Discharge Signals effectively and the correctness of the complex wavelets packet’s construction.
Four common simulative signals are disassembled and constructed, the resemble degree analysis on simulative signals with different frequency and strength narrow band noises before and after construction, it is shown that complex wavelets packet transform suppress unsmooth pulse signals more effectively revert signals more powerful.
According to the differences between PD signals and the narrow band noise after complex wavelet packet transform, this paper presented the specific combined information of serial WTRIn, which was used to suppress narrow band noise. Through the contrat of suppressing narrow band niose, it was proven that WTRIn had more powerful than simple information and other combined-informations. At last, through the analysis of the impacts of the index n on WTRIn it was shown that WTRI is the-best-denoising-combined-information no matter what frequency and intensity of the narrow band noise.
At last, this paper denoised PD admixture simulative and measured signals with different frequency and strength noises by complex wavelets packet transform, overcomed the difficult of more loss using hardware filter and bad effect using wavelets packet transformation when narrow band noise in the monitoring frequency band.
引文
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