粒子群优化小波自适应阈值法用于局部放电去噪

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摘要为了提高局部放电在线监测中小波去噪的自适应能力, 并降低去噪信号的畸变率, 提出了一种用于电力设备局部放电信号去噪的粒子群优化小波自适应阈值方法。该方法采用小波对局部放电信号进行分解, 在阈值选择时采用基于SURE无偏估计的最优阈值自适应选择方法, 结合粒子群优化算法进行全局自适应搜索最优阈值, 使最优阈值自适应寻优速度大大提高。为了验证其去噪效果, 还引入遗传算法对小波自适应阈值法进行优化计算。对局部放电仿真信号与实测局部放电信号的去噪结果表明, 本文与标准软阈值法和遗传算法优化小波自适应阈值法相比, 能更好地去除局部放电信号中的白噪声, 计算速度更快, 具有良好的去噪效果和应用价值。方法

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	江天炎
	李剑
	杜林
	王有元
	杨丽君

关键词 ：局部放电, 在线监测, 小波去噪, 自适应阈值, 粒子群优化算法, 局部放电, 在线监测, 小波去噪, 自适应阈值, 粒子群优化算法

Abstract：For the purpose of improving adaptive performance of wavelet de-noising and reducing distortion of de-noised signal, this paper presents an approach of particle swarm optimization (PSO) adaptive wavelet threshold estimation (PSOTE) for de-noising of partial discharge (PD) signals. The wavelet de-nosing algorithm is based on an optimum and adaptive shrinkage scheme. A class of shrinkage functions with continuous derivatives and PSO algorithm are utilized for the adaptive shrinkage scheme. The PSO algorithm is competent to obtain the global optimum thresholds and to raise the efficiency of adaptive searching computation. For verifying the de-noising results, genetic algorithm is adopted to optimize the wavelet threshold. The de-noising results of simulative PD signals and the field PD signals are presented. The results show that the white noise can be removed effectively by the PSOTE, the distortion of which is smaller than the signals de-noised by the standard soft threshold estimation (STE) and genetic adaptive wavelet threshold estimation (GTE). Meanwhile, the PSOTE is a much less time-consuming scheme and exhibits a promising prospect in practical application.

Key words： Partial discharge online monitoring wavelet de-noising adaptive thresholding particle swarm optimization algorithm Partial discharge online monitoring wavelet de-noising adaptive thresholding particle swarm optimization algorithm

收稿日期: 2010-10-13 出版日期: 2014-03-20

PACS:

TM411

基金资助:国家创新研究群体基金(51021005)和国家重点基础研究发展计划(973计划)(2009CB724508)资助项目。

作者简介: 江天炎男, 1987年生, 博士研究生, 研究方向为电气设备在线监测与智能化故障诊断技术。李剑男, 1971年生, 博士, 教授, 博士生导师, 长期从事电气设备在线监测与智能化故障诊断、绝缘老化和电工绝缘材料研究。

引用本文:

江天炎, 李剑, 杜林, 王有元, 杨丽君. 粒子群优化小波自适应阈值法用于局部放电去噪[J]. 电工技术学报, 2012, 27(5): 77-83. Jiang Tianyan, Li Jian, Du Lin, Wang Youyuan, Yang Lijun. De-Noising for Partial Discharge Signals Using PSO Adaptive Wavelet Threshold Estimation. Transactions of China Electrotechnical Society, 2012, 27(5): 77-83.

链接本文:

http://dgjsxb.ces-transaction.com/CN/Y2012/V27/I5/77

[1] 董明, 严璋, 杨莉, 等. 基于证据推理的电力变压器故障诊断策略[J]. 中国电机工程学报, 2006, 26(1): 106-114.
[2] Jian Li, Tianyan Jiang, Stanislaw Grzybowski, et al. Scale dependant wavelet selection for de-noising of partial discharge detection[J]. IEEE Transaction on Dielectrics and Electrical Insulation, 2010, 17(6): 1705-1714.
[3] U Kopf, K Feser. Rejection of narrow-band noise and repetitive pulses in on-site PD measurements[J]. IEEE Transaction on Dielectrics and Electrical Insulation, 1995, 2(6):433-446.
[4] 刘双宝, 吕超, 王立欣, 等. 应用组合广义形态学滤波器抑制变压器局放信号窄带干扰[J]. 高电压技术, 2009, 35(9): 2163-2168.
[5] 郭亮, 黄宏新, 成永红, 等. 嵌入式局部放电检测系统的数字滤波器设计[J]. 高电压技术, 2007, 33(8): 44-47, 55.
[6] Ma X, Zhou C, Kemp I J. Automated wavelet selection and thresholding for PD detection[J]. IEEE Electrical Insulation Magazine, 2002, 18(1): 37-45.
[7] 彭莉, 唐炬, 张晓星, 等. 一种基于复小波变换提取PD 信号的分块自适应复阈值算法[J]. 电工技术学报, 2008, 23(7): 112-117.
[8] 唐炬, 谢颜斌, 周倩, 等. 用于复小波变换去噪的复阈值构造算法[J]. 电工技术学报, 2008, 23(10): 121-128.
[9] 刘卫东, 刘尚合, 胡小锋, 等. 小波阈值去噪函数的改进方法分析[J]. 高电压技术, 2007, 33(10): 59-63.
[10] Chang C S, Jin J, Jumar S, et al. Denoising of partial discharge signals in wavelet packets domain [J]. IEE Proceedings Science, Measurement and Technology, 2005, 152(3): 129-140.
[11] Xie Yanbin, Tang Ju, Zhang Xiao-xing. Application of complex daubechies wavelet in UHF partial discharge measurements[J]. High Voltage Engineering, 2008, 34(12): 2701-2707.
[12] 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.
[13] Li Jian, Grzybowski S, Du Lin, et al. Extraction of partial discharges from noises by use of wavelet and pulse-sequence analysis [C]. 2006 IEEE Conference on Electrical Insulation and Dielectric Phenomena, Kansas City Missouri, 2006: 656-659.
[14] 王晓蓉, 杨敏中, 董明, 等. 基于神经网络的局部放电脉冲特征参数选择方法[J]. 电工技术学报, 2002, 17(3): 72-76.
[15] Chang C S, Jin J, Chang C, et al. Separation of corona using wavelet packet transform and neural network for detection of partial discharge in gas-insulated substations [J]. IEEE Transactions on Power Delivery, 2005, 20(2): 1363-1369.
[16] Donoho D L. De-noising by soft-thresholding[J]. IEEE Transactions on Information Theory, 1995, 41(3): 613-627.
[17] Zhang X P, Desai M D. Adaptive denoising based on sure risk [J]. IEEE Signal Processing Letters, 1998, 5(10): 265-267.
[18] 李剑, 程昌奎, 江天炎, 等. 遗传算法用于局部放电小波自适应阈值去噪[J]. 高电压技术, 2009, 35(9): 2114-2119.
[19] Chia-Nan Ko, Ying-Pin Chang, and Chia-Ju Wu. A PSO method with nonlinear time-varying evolution for optimal design of harmonic filters[J]. IEEE Transactions on Power Systems, 2009, 24(1): 437-444.