提升双树复小波在GIS局部放电监测白噪声抑制的应用

Application of White-noise Suppression in Gas Insulated Switchgear Partial Discharge Monitoring Based on Lifting Dual-tree Complex Wavelet Transform

GIS局部放电在线监测过程中原本就微弱的放电信号往往会被白噪声所淹没,因此抑制白噪声是GIS局部放电在线监测工作中必不可少的环节。为了更加有效且快速地抑制局放在线监测中白噪声的干扰,针对第1代小波去噪算法存在着去噪效果不佳、计算效率低等不足之处,结合双树复小波变换良好的去噪能力和提升小波变换高效的计算能力,提出对双树复小波变换进行提升,得到第二代双树复小波变换,应用提升双树复小波变换进行白噪声抑制研究,并且在局部放电在线监测软件系统中引入该算法,应用于现场监测实验。仿真和实验结果表明,较之其他去噪算法,提升双树复小波变换算法的运算速度提高了83%,内存占用降低了将近50%,信噪比得到了提高,且波形相似系数更接近1,更能满足GIS局部放电在线监测的实时性要求,在工程实践中具有更高的应用价值。

Weak partial discharge signals are always overwhelmed by white-noise in GIS partial discharge online monitoring. So how to suppress white-noise plays an important part in GIS partial discharge online monitoring. However, the first generation wavelet denoising algorithm has a large amount of calculation and poor denoising efficiency. In order to suppress white-noise more efficiently and quickly, we put forward a method that the lifting dual-tree complex wavelet is transformed into the white-noise suppression research because of the disadvantages of the first generation wavelet denoising algorithm. This algorithm is obtained by lifting the first generation dual-tree complex wavelet transform, and it combines the excellent denoising ability of complex wavelet transform and the high calculation efficiency of lifting wavelet transform. Then the online monitoring soft software system uses this algorithm as the core of calculation module to do field monitoring experiment. The results of simulation and experiment show that, compared with other denoising algorithms, the lifting dual-tree complex wavelet transform calculation speed is increased by 83%, memory occupancy is reduced by nearly 50 percent, the signal-to-noise ratio is improved, and waveform similarity coefficient is much closer to 1, which can better meet the the requirements of real-time in GIS partial discharge online monitoring, and has higher application value in the engineering practice.