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基于小波分解尺度系数能量最大原则的GIS局部放电超高频信号自适应小波去噪

, PP. 84-91

Keywords: 局部放电,超高频信号,小波去噪,自适应,波形畸变

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Abstract:

抑制干扰是GIS局部放电在线监测的关键技术之一。尽管局部放电超高频检测方法能够有效避开低频干扰,但来自测量系统的白噪声仍然为准确测量局部放电带来困难。为有效抑制白噪声,提高局部放电超高频法的测量精度,本文提出一种用于GIS局部放电超高频信号的自适应小波分解去噪算法,该算法基于每层小波分解尺度系数能量最大的原则,逐层自适应选取最优的小波进行分解,并结合Donoho提出的软阈值法进行去噪。对人工绝缘缺陷产生的四种GIS超高频信号的去噪结果证明了该算法较其他小波算法能更好地去除白噪声且去噪后信号波形畸变较小,具有很好的应用前景。

References

[1]  Sun Caixin, Xu Gaofeng, Tang Ju, et al.Model and performance of inner sensors used for partial discharge detection in GIS[J]. Proceedings of the CSEE, 2004, 24(8): 89-94.
[2]  R Kurrer, K Feser. The application of ultra-high- frequency partial discharge measurements to GIS-insulated substations[J]. IEEE Transaction on Power Delivery, 1998, 13(3): 777-782.
[3]  S Sriram, S Nitin, K M M Prabhu, et al. Signal denoising techniques for partial discharge measurements[J]. IEEE Transactions on Dielectrics and Electrical Insulation, 2005, 12(6): 1182-1191.
[4]  王立欣, 诸定秋, 蔡维铮. 局部放电在线监测中基于小波变换的阈值消噪算法研究[J]. 电网技术, 2003, 27(4): 46-48.
[5]  李剑, 孙才新, 杨霁, 等. 局部放电在线监测中小波阈值去噪法的最优阈值自适应选择[J]. 电网技术, 2006, 30(8): 25-30.
[6]  唐炬, 孙才新, 宋胜利, 等. 局部放电信号中的白噪声和窄带干扰[J]. 高电压技术, 2002, 28(12): 8-10.
[7]  刘庆, 张炳达, 李志兴. 利用最优小波进行局部放电脉冲的提取和消噪[J]. 电力系统及其自动化学报, 2003, 15(3):42-45.
[8]  李剑, 杨洋, 程昌奎, 等. 变压器局部放电监测逐层最优小波去噪算法[J]. 高电压技术, 2007, 33(8): 57-60.
[9]  Daubrchies I.Ten lectures on wavelets[M]. America: Society for Industrial and Applied Mathematics, 1992.
[10]  唐炬. 组合电器局放在线监测外置传感器和复小波抑制干扰的研究[D]. 重庆: 重庆大学, 2004.
[11]  Adamiak K Atten P. Simulation of corona discharge in point-plane configuration[J]. Journal of Electrostatics, 2004, 61: 85-98.
[12]  M D Judd, et al. Broadband couplers for UHF detection of partial discharge in gas-insulated substations[J]. IEE Proceedings-Science Measurement and Technology, 1995, 142(3): 237-243.
[13]  孙才新, 许高峰, 唐炬, 等. 检测GIS局部放电的内置传感器的模型及性能研究[J]. 中国电机工程学报, 2004, 24(8): 89-94.
[14]  D L Donoho. De-noising by soft-thresholding [J]. IEEE Transaction on Information Theory, 1995, 41(3): 613-627.
[15]  刘双宝, 陶善宏, 于继来, 等. 提取 PD 信号的复小波簇消噪算法[J]. 高电压技术, 2007, 33(10): 69-72.
[16]  Liu Shuangbao, Tao Shanhong, Yu Jilai, at al. Algorithm of complex wavelet cluster for extracting PD signal[J]. High Voltage Engineering, 2007, 33(10): 69-72.
[17]  Wang Lixin, Zhu Dingqiu, Cai Weizheng. Wavelet transform based de-noising algorithm by thresholding in on-line partial discharge detection[J]. Power System Technology, 2003, 27(4): 46-48.
[18]  Li Jian, Sun Caixin, Yang Ji, et al. Adaptive optimal threshold selection of wavelet-based threshold de-noising for on-line partial discharge monitoring[J]. Power System Technology, 2006, 30(8): 25-30.
[19]  Tang Ju, Sun Caixin, Song Shengli, et al. Application of wavelet packet transform to the suppression of white-noise and periodic narrowband interference in partial discharge signals[J]. High Voltage Engineering, 2002, 28(12): 8-10.
[20]  Liu Qing, Zhang Bingda, Li Zhixing. Extracttion and noise elimination of partial discharge signal by using optimal wavelet[J]. Proceedings of the CSU EPSA, 2003, 15(3): 42-45.
[21]  X Ma, C Zhou, I J Kemp. Automated wavelet selection and thresholding for PD detection[J]. IEEE Electrical Insulation Magazine, 2002, 18(1): 37-45.
[22]  S Sriram, S Nitin, K M M Prabhu, et al. Signal denoising techniques for partial discharge measurements[J]. IEEE Transactions on Dielectrics and Electrical Insulation, 2005, 12(6): 1182-1191.
[23]  Li Jian, Yang Yang, Cheng Changkui, et al. Optimum wavelet de-noising algorithm for partial discharge online monitoring of transformers[J]. High Voltage Engineering, 2007, 33(8): 57-60.
[24]  Mallat S. A wavelet tour of signal processing[M]. San Diego: Academic Press, 1998.

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