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

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Abstract Interference suppression was one of the key technologies in on-line partial discharge(PD) monitoring of gas insulated switchgear(GIS). Although ultra-high-frequency (UHF) is qualified to avoid low-frequency noises, the system white noise from the high voltage transmission line still make it difficult to accurately measure the level of PD. For active inhibition of the white noise interference and improving the precision of the UHF detection methods, this paper presents a adaptive de-noising scheme, which is suitable for de-noising UHF signal detected by the UHF detection system of PD in GIS. The method utilizes various basic wavelet to decompose a signal, and calculate and compare the signal energies caused by decomposition using different wavelets in each scale. The basic wavelet corresponding to the maximum signal energy is considered as the optimum wavelet in the current scale, thus the optimum wavelets family of all the scales is obtained, and the soft threshold function presented by Donoho is used to de-nosing . The result of de-noising a UHF signal generated by an artificial insulation defect convinces that the adaptive wavelet de-noising method is more effective to suppress the white noise mixed in UHF signal than the other wavelet-based de-noising method, it has an good practical value in on-line PD monitoring of GIS.

Key words： Partial discharge ultra-high-frequency signal wavelet de-noising method adaptive waveform distortion

Received: 28 December 2010 Published: 20 March 2014

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TM835

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	Li Hua
	Yang Xinchun
	Li Jian
	Chen Jiao
	Cheng Changkui

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Li Hua,Yang Xinchun,Li Jian等. The Maximum Energy of Wavelet Decomposition Approximation -Related Adaptive Wavelet De-Nosing for Partial Discharge UHF Pulse in GIS[J]. Transactions of China Electrotechnical Society, 2012, 27(5): 84-91.

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http://dgjsxb.ces-transaction.com/EN/ OR http://dgjsxb.ces-transaction.com/EN/Y2012/V27/I5/84

[1] 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.
[2] 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.
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.
[3] 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.
[4] D L Donoho. De-noising by soft-thresholding [J]. IEEE Transaction on Information Theory, 1995, 41(3): 613-627.
[5] 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.
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.
[6] 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.
[7] 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.
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.
[8] 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.
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.
[9] 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.
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.
[10] 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.
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.
[11] 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.
[12] 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.
[13] 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.
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.
[14] Mallat S. A wavelet tour of signal processing[M]. San Diego: Academic Press, 1998.
[15] Daubrchies I.Ten lectures on wavelets[M]. America: Society for Industrial and Applied Mathematics, 1992.
[16] 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.
[17] Adamiak K Atten P. Simulation of corona discharge in point-plane configuration[J]. Journal of Electrostatics, 2004, 61: 85-98.