相关概率小波变换在局部放电检测中的应用
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摘要
为准确识别高压电气设备中被噪声淹没的局部放电信号,提出了一种基于相关概率小波变换的局部放电信号检测方法。该方法首先对采集到的原始信号进行小波变换,利用局部放电信号与噪声信号相关性的不同对各层的小波系数进行预处理,然后基于分位数的概念在处理后的各分解层上设置若干个多尺度阈值,根据这些阈值计算原始信号各处为局部放电信号的概率,最后根据概率值的大小来判断该处是否发生局部放电。利用该方法对仿真及实测信号进行分析,并与传统小波变换方法的处理结果进行比较。结果表明,该方法能够更为有效地抑制局部放电在线监测中的噪声干扰,全面、可靠地检测到强噪声背景下的微弱局部放电信号,具有一定的工程应用价值。
In order to accurately identify the partial discharge signal overwhelmed by noise in high voltage electrical equipment, a method based on correlated probabilistic wavelet transform is proposed for the accurate detection of partial discharge signal in this paper. First, the original signal measured is decomposed by wavelet transform, and the wavelet coefficients of each level is preprocessed according to the difference of correlation between the partial discharge signal and noise signal, and then the multi-scale thresholds at each processed decomposition level are set based on quantile, and the probability which indicates the possibility of each point in the original signal to be partial discharge signal is computed based on these thresholds, finally determine whether there is partial discharge or not according to its probability. The method presented in this paper was applied on the simulation and measurement signal, and compared with the result of traditional wavelet transform. The results show that the method can suppress the noise more effectively in partial discharge online monitoring, which are more comprehensive and reliable to detect weak partial discharge signal in the strong noise background, and has certain engineering value.
In order to accurately identify the partial discharge signal overwhelmed by noise in high voltage electrical equipment, a method based on correlated probabilistic wavelet transform is proposed for the accurate detection of partial discharge signal in this paper. First, the original signal measured is decomposed by wavelet transform, and the wavelet coefficients of each level is preprocessed according to the difference of correlation between the partial discharge signal and noise signal, and then the multi-scale thresholds at each processed decomposition level are set based on quantile, and the probability which indicates the possibility of each point in the original signal to be partial discharge signal is computed based on these thresholds, finally determine whether there is partial discharge or not according to its probability. The method presented in this paper was applied on the simulation and measurement signal, and compared with the result of traditional wavelet transform. The results show that the method can suppress the noise more effectively in partial discharge online monitoring, which are more comprehensive and reliable to detect weak partial discharge signal in the strong noise background, and has certain engineering value.
引文
[1]云玉新,赵笑笑,李世鹏,等.基于快速独立分量分析算法的气体绝缘开关设备局部放电混合信号分离与缺陷类型辨识[J].高电压技术,2014,40(3):853-860.YUN Yuxin,ZHAO Xiaoxiao,LI Shipeng,et al.Separation of partial discharge mixing signals and type identification of defects in gas insulated switchgear based on fast independent component analysis algorithm[J].High Voltage Engineering,2014,40(3):853-860.
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[5]杜林,严金平,王红梅,等.基于电平扫描法的超高频局部放电测量方法的特性分析[J].高电压技术,2014,40(8):2299-2305.DU Lin,YAN Jinping,WANG Hongmei,et al.Characteristics analysis of ultra-high-frequency partial discharge measurement method based on level scanning method[J].High Voltage Engineering,2014,40(8):2299-2305.
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[7]唐炬,董玉林,樊雷,等.基于Hankel矩阵的复小波-奇异值分解法提取局部放电特征信息[J].中国电机工程学报,2015,35(7):1808-1817.TANG Ju,DONG Yulin,FAN Lei,et al.Feature information extraction of partial discharge with complex wavelet transform and singular value decomposition based on hankel matrix[J].Proceedings of the CSEE,2015,35(7):1808-1817.
[8]龙虹毓,张晓勇,胡晓锐,等.蚁群优化小波阈值算法用于变电设备状态信号提取[J].电工技术学报,2015,30(12):422-428.LONG Hongyu,ZHANG Xiaoyong,HU Xiaorui,et al.Extraction of condition signals of electrical plants by ACO wavelet threshold estimation[J].Transactions of China Electrotechnical Society,2015,30(12):422-428.
[9]黄成军,郁惟镛.基于小波分解的自适应滤波算法在抑制局部放电窄带周期干扰中的应用[J].中国电机工程学报,2003,23(1):107-111.HUANG Chengjun,YU Weiyong.Study of adaptive filter algorithm based on wavelet analysis in suppressing PD’s periodic narrow bandwidth noise[J].Proceedings of the CSEE,2003,23(1):107-111.
[10]JUNHYUCK S,HUI M,TAPAN S.Probabilistic wavelet transform for partial discharge measurement of transformer[J].IEEE Transactions on Dielectrics and Electrical Insulation,2015,22(2):1106-1117.
[11]XU Y S,WEAVER J B.Wavelet transform domain filters:a spatially selective noise filtration technique[J].IEEE Transactions on Image Processing,1994,3(6):747-758.
[12]彭云辉,刘云峰,杨小冈,等.基于空域相关法的激光陀螺信号滤波方法研究[J].红外与激光工程,2007,36(4):493-496.PENG Yunhui,LIU Yunfeng,YANG Xiaogang,et al.Spatial correlation de-noising for signal of laser gyro[J].Infrared and Laser Engineering,2007,36(4):493-496.
[13]贾毅婷,张东来,张斌.基于小波尺度相关性的暂态数据降噪压缩方法[J].电力系统自动化,2013,37(5):68-73.JIA Yiting,ZHANG Donglai,ZHANG Bin.Power quality disturbance data denoising and compression using signal’s scale-dependencies wavelet cofficients[J].Automation of Electric Power Systems,2013,37(5):68-73.
[14]王刘旺,朱永利,李莉,等.基于自适应双阈值的局部放电基本参数提取[J].高电压技术,2016,42(4):1268-1274.WANG Liuwang,ZHU Yongli,LI Li,et al.Extraction of fundamental parameters in partial discharge based on adaptive dual threshold[J].High Voltage Engineering,2016,42(4):1268-1274.
[15]姚陈果,乔盼盼,陈攀,等.用于电气设备局部放电超高频信号监测的0.4~1 GHz分形天线[J].高电压技术,2014,40(8):2285-2291.YAO Chenguo,QIAO Panpan,CHEN Pan,et al.Fractal antenna of0.4~1 GHz for UHF monitoring of partial discharge in electrical equipment[J].High Voltage Engineering,2014,40(8):2285-2291.
[16]罗青,牛海清,胡日亮,等.一种改进的用于快速傅里叶变换功率谱中的窄带干扰抑制的方法[J].中国电机工程学报,2013,33(12):167-175.LUO Qing,NIU Haiqing,HU Riliang,et al.A modified method of suppressing narrow-band interference using fast Fourier transform power spectrum[J].Proceedings of the CSEE,2013,33(12):167-175.
[17]张晓星,周君杰,李楠,等.抑制局部放电白噪声的分块阈值空域相关联合去噪法[J].高电压技术,2011,37(5):1142-1148.ZHANG Xiaoxing,ZHOU Junjie,LI Nan,et al.Block thresholding spatial combined de-noising method for suppress white-noise interference in PD signals[J].High Voltage Engineering,2011,37(5):1142-1148.
[18]贾嵘,徐其惠,田录林,等.基于经验模态分解的水轮发电机组局部放电信号提取[J].水利发电学报,2007,26(4):146-150.JIA Rong,XU Qihui,TIAN Lulin,et al.Extracting partial discharge signals of hydroelectric generating set based on empirical mode decomposition[J].Journal of Hydroelectric Generating,2007,26(4):146-150.
[19]尚海昆,苑津莎,王瑜,等.平移不变小波迹消噪方法在局部放电检测中的应用[J].电工技术学报,2013,28(10):33-40.SHANG Haikun,YUAN Jinsha,WANG Yu,et al.Application of wavelet footprints based on translation-invariant in of partial discharge signal detection[J].Transactions of China Electrotechnical Society,2013,28(10):33-40.
[20]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.
[21]江天炎,李剑,杜林,等.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,et al.De-noising or partial discharge signals using PSO adaptive wavelet threshold estimation[J].Transactions of China Electrotechnical Society,2012,27(5):77-83.
[2]李军浩,韩旭涛,刘泽辉,等.电气设备局部放电检测技术述评[J].高电压技术,2015,41(8):2583-2601.LI Junhao,HAN Xutao,LIU Zehui,et al.Review on partial discharge measurement technology of electrical equipment[J].High Voltage Engineering,2015,41(8):2583-2601.
[3]MA H,CHAN J C,SAHA T,et al.Pattern recognition techniques and their applications for automatic partial discharge source classification[J].IEEE Transactions on Dielectrics and Electrical Insulation,2013,20(2):468-478.
[4]贾嵘,徐其惠,田录林,等.基于经验模态分解和固有模态函数重构的局部放电去噪方法[J].电工技术学报,2008,23(1):14-18.JIA Rong,XU Qihui,TIAN Lulin,et al.Denoising of partial discharge based on empirical mode decomposition and intrinsic mode function reconstruction[J].Transactions of China Electrotechnical Society,2008,23(1):14-18.
[5]杜林,严金平,王红梅,等.基于电平扫描法的超高频局部放电测量方法的特性分析[J].高电压技术,2014,40(8):2299-2305.DU Lin,YAN Jinping,WANG Hongmei,et al.Characteristics analysis of ultra-high-frequency partial discharge measurement method based on level scanning method[J].High Voltage Engineering,2014,40(8):2299-2305.
[6]尚海昆,苑津莎,王瑜,等.基于交叉小波变换和相关系数矩阵的局部放电特征提取[J].电工技术学报,2014,29(4):274-281.SHANG Haikun,YUAN Jinsha,WANG Yu,et al.Feature extraction for partial discharge based on cross-wavelet transform and correlation coefficient matrix[J].Transactions of China Electrotechnical Society,2014,29(4):274-281.
[7]唐炬,董玉林,樊雷,等.基于Hankel矩阵的复小波-奇异值分解法提取局部放电特征信息[J].中国电机工程学报,2015,35(7):1808-1817.TANG Ju,DONG Yulin,FAN Lei,et al.Feature information extraction of partial discharge with complex wavelet transform and singular value decomposition based on hankel matrix[J].Proceedings of the CSEE,2015,35(7):1808-1817.
[8]龙虹毓,张晓勇,胡晓锐,等.蚁群优化小波阈值算法用于变电设备状态信号提取[J].电工技术学报,2015,30(12):422-428.LONG Hongyu,ZHANG Xiaoyong,HU Xiaorui,et al.Extraction of condition signals of electrical plants by ACO wavelet threshold estimation[J].Transactions of China Electrotechnical Society,2015,30(12):422-428.
[9]黄成军,郁惟镛.基于小波分解的自适应滤波算法在抑制局部放电窄带周期干扰中的应用[J].中国电机工程学报,2003,23(1):107-111.HUANG Chengjun,YU Weiyong.Study of adaptive filter algorithm based on wavelet analysis in suppressing PD’s periodic narrow bandwidth noise[J].Proceedings of the CSEE,2003,23(1):107-111.
[10]JUNHYUCK S,HUI M,TAPAN S.Probabilistic wavelet transform for partial discharge measurement of transformer[J].IEEE Transactions on Dielectrics and Electrical Insulation,2015,22(2):1106-1117.
[11]XU Y S,WEAVER J B.Wavelet transform domain filters:a spatially selective noise filtration technique[J].IEEE Transactions on Image Processing,1994,3(6):747-758.
[12]彭云辉,刘云峰,杨小冈,等.基于空域相关法的激光陀螺信号滤波方法研究[J].红外与激光工程,2007,36(4):493-496.PENG Yunhui,LIU Yunfeng,YANG Xiaogang,et al.Spatial correlation de-noising for signal of laser gyro[J].Infrared and Laser Engineering,2007,36(4):493-496.
[13]贾毅婷,张东来,张斌.基于小波尺度相关性的暂态数据降噪压缩方法[J].电力系统自动化,2013,37(5):68-73.JIA Yiting,ZHANG Donglai,ZHANG Bin.Power quality disturbance data denoising and compression using signal’s scale-dependencies wavelet cofficients[J].Automation of Electric Power Systems,2013,37(5):68-73.
[14]王刘旺,朱永利,李莉,等.基于自适应双阈值的局部放电基本参数提取[J].高电压技术,2016,42(4):1268-1274.WANG Liuwang,ZHU Yongli,LI Li,et al.Extraction of fundamental parameters in partial discharge based on adaptive dual threshold[J].High Voltage Engineering,2016,42(4):1268-1274.
[15]姚陈果,乔盼盼,陈攀,等.用于电气设备局部放电超高频信号监测的0.4~1 GHz分形天线[J].高电压技术,2014,40(8):2285-2291.YAO Chenguo,QIAO Panpan,CHEN Pan,et al.Fractal antenna of0.4~1 GHz for UHF monitoring of partial discharge in electrical equipment[J].High Voltage Engineering,2014,40(8):2285-2291.
[16]罗青,牛海清,胡日亮,等.一种改进的用于快速傅里叶变换功率谱中的窄带干扰抑制的方法[J].中国电机工程学报,2013,33(12):167-175.LUO Qing,NIU Haiqing,HU Riliang,et al.A modified method of suppressing narrow-band interference using fast Fourier transform power spectrum[J].Proceedings of the CSEE,2013,33(12):167-175.
[17]张晓星,周君杰,李楠,等.抑制局部放电白噪声的分块阈值空域相关联合去噪法[J].高电压技术,2011,37(5):1142-1148.ZHANG Xiaoxing,ZHOU Junjie,LI Nan,et al.Block thresholding spatial combined de-noising method for suppress white-noise interference in PD signals[J].High Voltage Engineering,2011,37(5):1142-1148.
[18]贾嵘,徐其惠,田录林,等.基于经验模态分解的水轮发电机组局部放电信号提取[J].水利发电学报,2007,26(4):146-150.JIA Rong,XU Qihui,TIAN Lulin,et al.Extracting partial discharge signals of hydroelectric generating set based on empirical mode decomposition[J].Journal of Hydroelectric Generating,2007,26(4):146-150.
[19]尚海昆,苑津莎,王瑜,等.平移不变小波迹消噪方法在局部放电检测中的应用[J].电工技术学报,2013,28(10):33-40.SHANG Haikun,YUAN Jinsha,WANG Yu,et al.Application of wavelet footprints based on translation-invariant in of partial discharge signal detection[J].Transactions of China Electrotechnical Society,2013,28(10):33-40.
[20]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.
[21]江天炎,李剑,杜林,等.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,et al.De-noising or partial discharge signals using PSO adaptive wavelet threshold estimation[J].Transactions of China Electrotechnical Society,2012,27(5):77-83.