粒子群算法用于局部放电小波阈值去噪
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摘要
小波阈值去噪在局放信号监测分析方面应用效果较好,而小波系数阈值的选取是决定局放信号去噪后的失真和误差的关键因素。针对局放脉冲频谱特征,提出了一种基于粒子群算法的小波最优阈值选择方法,用于局放脉冲信号去噪。采用小波对局放信号进行分解,在估计最优阈值时以广义交叉验证为标准,利用粒子群算法进行全局搜索,使阈值寻优效果大大提升。对人工模拟加噪信号和典型局放脉冲仿真信号进行去噪处理和定量分析,结果表明与标准软阈值法以及Donoho阈值计算法相比,对局放信号的去噪效果更好,有着非常好的应用前景和价值。
Wavelet threshold de-noising produces a good effect when it is applied to the monitoring and analysis of partial discharge( PD)signals,and selection of the threshold for the wavelet coefficient is a key factor for determining the distortion and error of the de-noised PD signal. Under consideration of the characteristics of PD pulse spectrum,this paper proposed an optimal wavelet threshold selection method based on the particle swarm optimization( PSO) for de-noising of PD pulse signals. The wavelet was used to decompose PD signal,and generalized cross validation( GCV) was adopted as the criterion in the estimation of the optimal threshold.PSO was used for global search to greatly improve the threshold optimization effect. De-noising and quantitative analysis was conducted on the artificially simulated noise-adding signal and the typical PD pulse simulation signal. The results show that,compared with standard soft threshold method or Donoho threshold calculation method,better de-noising effect is produced with PD signals and that has a good application prospect and value.
Wavelet threshold de-noising produces a good effect when it is applied to the monitoring and analysis of partial discharge( PD)signals,and selection of the threshold for the wavelet coefficient is a key factor for determining the distortion and error of the de-noised PD signal. Under consideration of the characteristics of PD pulse spectrum,this paper proposed an optimal wavelet threshold selection method based on the particle swarm optimization( PSO) for de-noising of PD pulse signals. The wavelet was used to decompose PD signal,and generalized cross validation( GCV) was adopted as the criterion in the estimation of the optimal threshold.PSO was used for global search to greatly improve the threshold optimization effect. De-noising and quantitative analysis was conducted on the artificially simulated noise-adding signal and the typical PD pulse simulation signal. The results show that,compared with standard soft threshold method or Donoho threshold calculation method,better de-noising effect is produced with PD signals and that has a good application prospect and value.
引文
[1]全国高电压试验技术和绝缘配合标准化技术委员会.GB/T 7354-2003局部放电测量[S].北京:国家质量监督检验检疫总局,2003.
[2]杨森,熊俊,郑服利,等.基于特高频无线智能传感器的局部放电定位法[J].电气自动化,2017,39(2):110-112,115.
[3]魏翀,熊俊,杨森.GIS局部放电带电检测技术分析与现场应用[J].电气自动化,2016,38(2):106-108,114.
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[5]王永强,谢军,律方成,等.基于改进量子粒子群优化稀疏分解的局放信号去噪方法[J].电工技术学报,2015,30(12):320-329.
[6]刘红霞,钱勇,姚林朋,等.基于模糊聚类的XLPE电缆随机脉冲干扰抑制方法研究[J].电气自动化,2011,33(2):76-78,81.
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[8]孙振华,冯金茏,欧阳力,等.开关柜超声波局放检测抗干扰方法研究[J].湖南电力,2016,36(2):46-49.
[9]马振祺,刘克发,温定筠,等.超高压电缆局放在线监测局放信号提取技术研究及应用[J].电测与仪表,2016,53(18):93-99.
[10]彭炜文,田慕琴,宋建成,等.一种基于EEMD和高阶统计量的局部放电白噪声抑制方法[J].高压电器,2016,52(5):54-59,64.
[11]DONOHO D L.De-noising by soft-thresholding[J].IEEE Transactions on Information Theory,1995,41(3):613-627.
[12]曹京京,胡辽林,赵瑞.一种改进小波阈值函数的光纤光栅传感信号去噪方法[J].传感技术学报,2015,28(4):521-525.
[13]韩子扬,杜利明,王鑫,等.一种改进的小波阈值去噪方法[J].现代电子技术,2016,39(17):20-22,25.
[14]李新春,吴秋铜.基于改进遗传算法的小波阈值语音去噪[J].激光杂志,2015,36(1):62-65.
[15]刘洲峰,徐庆伟,李春雷,等.小波变换在图像检测中的应用[J].电气自动化,2008,30(6):90-91.
[16]程昌奎,郑永建,庞军,等.变压器局部放电监测中的最优小波与脉冲鉴别去噪方法研究[J].高压电器,2016,52(3):123-128.
[17]CRAVEN P,WAHBA G.Smoothing noisy data with spline functions[J].Numerische Mathematik,1975,24(5):383-393.
[18]KENNEDY J,EBERHART R.Particle swarm optimization[C]//IEEE International Conference on Neural Networks,1995,Proceedings.IEEE Xplore,1995:1942-1948.
[19]任哲,陈怀亮,王连喜,等.利用交叉验证的小麦LAI反演模型研究[J].国土资源遥感,2015,27(4):34-40.
[20]李洪江,刘栋.基于交叉验证支持向量机的短期负荷预测[J].云南电力技术,2016,44(3):1-3,13.
[2]杨森,熊俊,郑服利,等.基于特高频无线智能传感器的局部放电定位法[J].电气自动化,2017,39(2):110-112,115.
[3]魏翀,熊俊,杨森.GIS局部放电带电检测技术分析与现场应用[J].电气自动化,2016,38(2):106-108,114.
[4]唐志国,蒋佟佟,刘昌标,等.高频电流法检测电容器局部放电的抗干扰研究[C]//中国电机工程学会高电压专业委员会2015年学术年会论文集,2015:1-6.
[5]王永强,谢军,律方成,等.基于改进量子粒子群优化稀疏分解的局放信号去噪方法[J].电工技术学报,2015,30(12):320-329.
[6]刘红霞,钱勇,姚林朋,等.基于模糊聚类的XLPE电缆随机脉冲干扰抑制方法研究[J].电气自动化,2011,33(2):76-78,81.
[7]刘大永,刘秀凤,李付有,等.局部放电检测抗干扰分析[J].硅谷,2015,14(4):57-57,38.
[8]孙振华,冯金茏,欧阳力,等.开关柜超声波局放检测抗干扰方法研究[J].湖南电力,2016,36(2):46-49.
[9]马振祺,刘克发,温定筠,等.超高压电缆局放在线监测局放信号提取技术研究及应用[J].电测与仪表,2016,53(18):93-99.
[10]彭炜文,田慕琴,宋建成,等.一种基于EEMD和高阶统计量的局部放电白噪声抑制方法[J].高压电器,2016,52(5):54-59,64.
[11]DONOHO D L.De-noising by soft-thresholding[J].IEEE Transactions on Information Theory,1995,41(3):613-627.
[12]曹京京,胡辽林,赵瑞.一种改进小波阈值函数的光纤光栅传感信号去噪方法[J].传感技术学报,2015,28(4):521-525.
[13]韩子扬,杜利明,王鑫,等.一种改进的小波阈值去噪方法[J].现代电子技术,2016,39(17):20-22,25.
[14]李新春,吴秋铜.基于改进遗传算法的小波阈值语音去噪[J].激光杂志,2015,36(1):62-65.
[15]刘洲峰,徐庆伟,李春雷,等.小波变换在图像检测中的应用[J].电气自动化,2008,30(6):90-91.
[16]程昌奎,郑永建,庞军,等.变压器局部放电监测中的最优小波与脉冲鉴别去噪方法研究[J].高压电器,2016,52(3):123-128.
[17]CRAVEN P,WAHBA G.Smoothing noisy data with spline functions[J].Numerische Mathematik,1975,24(5):383-393.
[18]KENNEDY J,EBERHART R.Particle swarm optimization[C]//IEEE International Conference on Neural Networks,1995,Proceedings.IEEE Xplore,1995:1942-1948.
[19]任哲,陈怀亮,王连喜,等.利用交叉验证的小麦LAI反演模型研究[J].国土资源遥感,2015,27(4):34-40.
[20]李洪江,刘栋.基于交叉验证支持向量机的短期负荷预测[J].云南电力技术,2016,44(3):1-3,13.