基于压缩传感的输电线路绝缘子泄漏电流数据压缩研究
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摘要
研究发现绝缘泄漏电流与绝缘子故障关系密切,能够科学地表征绝缘子绝缘水平。绝缘子泄漏电流尤其在闪络放电过程中含有大量高频脉冲分量,对信号的采样速率和处理速度要求很高,其庞大的采集数据量给数据通信和存储造成了严重的负担。研究发现绝缘子泄漏电流在某一变换域下是稀疏的,课题研究分析压缩传感理论实现对绝缘子泄漏电流的数据压缩。压缩传感以远远低于奈奎斯特采样定理的频率实现对高频绝缘子泄漏电流的采集,从而降低数据通信和存储负担,使高频信号采集成为可能。
本文研究分析傅立叶变换与小波变换对绝缘子泄漏电流的稀疏表示的效果,求出具有局部分辨性的小波变换较之全局性的傅立叶变换,更能简洁表示泄漏电流信号。对高斯测量矩阵的测量数与泄漏电流的稀疏度的关系进行深入研究,结合实验数据,得出测量数与稀疏度的大致关系。
围绕压缩传感理论对输电线路绝缘子泄漏电流先进行小波变换的稀疏表示,然后经过高斯测量矩阵线性投影获取测量值,最后由正交匹配追踪算法恢复原始信号。实验结果表明,由少量的测量值能够高精度地恢复原始信号,不仅降低对传感器采样频率的要求,而且极大地降低了数据存储和传输代价。
为了进一步提高信号重构的速度,在小波稀疏变换基础上,引入经验模态(EMD)分解方法对绝缘泄漏电流进行平稳化处理。改进方法得到实验结果与原有算法的结果对比,可以看出优化后的算法在提高重构运算速度的同时,也能够精确的重构原始信号。
Research finds that leakage current is closely related to flashover discharge, it can characterize external insulation status of insulator scientifically. Insulator leakage current especially in the flashover discharge process contains a lot of high-frequency pulse component,it requires the signal sampling rate and processing speed higher. So the huge data collection caused great burden on the data communication and storage.Found that leakage current is sparse under a certain transform domain, insulator leakage current was compressed in the subject based on compressed sensing theory. Sampling frequency that compressed sensing sampled high-frequency leakage current is far below the Nyquist sampling theorem,thus it reduces the burden of data communication and storage, making high-frequency signal acquisition possibly.
In this paper,we analyse insulation leakage current sparsity between the wavelet and fourier transform.From these results, wavelet transform represents the leakage current signal more succinct than fourier transform.Relation has be made study of the measurement and the sparsity in a deep-going way. We have found the approximate relationship between measurement and sparity by combining experimental data.
The simulation experiment encloses insulator leakage current in the transmission line based on compressed sensing, it first represents the non-stationary insulation leakage current sparse by wavelet transform, and then projects linear measurement by Gaussian measurement matrix, and finally adopts orthogonal matching pursuit algorithm to restore original signal.Experimental results show that the original signal can be recovered by a few of measurements with high precision.The algorithm not only reduces the required sampling frequency of the sensor, but also the data storage and transmission costs.
To further improve the reconstruction speed, empirical mode decomposition method is induced to process insulation leakage current stably after the analysis of limitations from the wavelet and Fourier transform processing non-stationary signals . Compressed sensing was applied to compression simulation experiment. We can see that the optimized algorithm not only improved reconstruction computing speed but also reconstructed original signal precisely by contrasting experimental results of original algorithm.
本文研究分析傅立叶变换与小波变换对绝缘子泄漏电流的稀疏表示的效果,求出具有局部分辨性的小波变换较之全局性的傅立叶变换,更能简洁表示泄漏电流信号。对高斯测量矩阵的测量数与泄漏电流的稀疏度的关系进行深入研究,结合实验数据,得出测量数与稀疏度的大致关系。
围绕压缩传感理论对输电线路绝缘子泄漏电流先进行小波变换的稀疏表示,然后经过高斯测量矩阵线性投影获取测量值,最后由正交匹配追踪算法恢复原始信号。实验结果表明,由少量的测量值能够高精度地恢复原始信号,不仅降低对传感器采样频率的要求,而且极大地降低了数据存储和传输代价。
为了进一步提高信号重构的速度,在小波稀疏变换基础上,引入经验模态(EMD)分解方法对绝缘泄漏电流进行平稳化处理。改进方法得到实验结果与原有算法的结果对比,可以看出优化后的算法在提高重构运算速度的同时,也能够精确的重构原始信号。
Research finds that leakage current is closely related to flashover discharge, it can characterize external insulation status of insulator scientifically. Insulator leakage current especially in the flashover discharge process contains a lot of high-frequency pulse component,it requires the signal sampling rate and processing speed higher. So the huge data collection caused great burden on the data communication and storage.Found that leakage current is sparse under a certain transform domain, insulator leakage current was compressed in the subject based on compressed sensing theory. Sampling frequency that compressed sensing sampled high-frequency leakage current is far below the Nyquist sampling theorem,thus it reduces the burden of data communication and storage, making high-frequency signal acquisition possibly.
In this paper,we analyse insulation leakage current sparsity between the wavelet and fourier transform.From these results, wavelet transform represents the leakage current signal more succinct than fourier transform.Relation has be made study of the measurement and the sparsity in a deep-going way. We have found the approximate relationship between measurement and sparity by combining experimental data.
The simulation experiment encloses insulator leakage current in the transmission line based on compressed sensing, it first represents the non-stationary insulation leakage current sparse by wavelet transform, and then projects linear measurement by Gaussian measurement matrix, and finally adopts orthogonal matching pursuit algorithm to restore original signal.Experimental results show that the original signal can be recovered by a few of measurements with high precision.The algorithm not only reduces the required sampling frequency of the sensor, but also the data storage and transmission costs.
To further improve the reconstruction speed, empirical mode decomposition method is induced to process insulation leakage current stably after the analysis of limitations from the wavelet and Fourier transform processing non-stationary signals . Compressed sensing was applied to compression simulation experiment. We can see that the optimized algorithm not only improved reconstruction computing speed but also reconstructed original signal precisely by contrasting experimental results of original algorithm.
引文
[1]袁力翔,曾奕,江秀成等.输电线路绝缘子综合在线监测系统[J].电工技术,2009,37(2):36-39
[2]关志成,毛颖科,王黎明.污秽绝缘子泄漏电流特性研究[J].高电压技术,2008,34(1):2-6
[3]李璟延,孙才新,司马文霞.染污绝缘子污闪全过程泄漏电流波形及谐波特性分析[J].高电压技术,2010,36(5):1101-1106
[4]惠阿丽,林辉,贺博.分形理论压缩和恢复高压绝缘子泄漏电流[J].高电压技术,2008,34(7):1395-1400
[5]孙秒平.电力系统故障录波数据压缩方法研究[J].电力自动化设备,2004,25(11):34-36
[6]黄险峰,钱路江.电力系统故障录波信号压缩[J].电力自动化设备,2001,21(11):18-21
[7]苗世洪,王少荣等.数据压缩技术在电力系统通信中的应用[J].电力自动化,1999,19(3):32-33
[8]石光明,刘丹华,高大化等.压缩感知理论及其研究进展[J].电子学报,2009,37(5):1070-1081
[9]李树涛,魏丹.压缩传感综述[J].自动化学报,2009,35(11): 1369-1377.
[10] Donoho D. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4): 1289-1306
[11]孙金凤.电力数据压缩传输及解压算法的研究与实现:[D].北京:北京化工大学,2008
[12]欧阳森,宋正湘,陈德桂.基于小波原理的电能质量检测数据实时压缩方法[J].电网技术, 2003,27(2):37-40
[13]李波,谢杰镇,王博亮.基于压缩传感理论的数据重建[J].计算机技术与发展,2009,19(5):23-25
[14]练秋生,郝鹏鹏.基于压缩传感的代数重建法德CT图像重建[J].光学技术,2009,35(3): 422-425
[15] Candes E, Wakin M B. An Introduction to Compressive Sampling [J].IEEE Signal Processing Magazine, 2008, 48(4): 21-30
[16] Candès E, Romberg J. Sparsity and incoherence in compressive sampling. Inverse Problems, 2007, 23(3): 969-985
[17]杜欣慧,戴云航,王志刚,宫改花.饱和受潮条件下的绝缘子泄漏电流特性[J].高电压技术,2007,33(9):6-9
[18]李毅.覆冰绝缘子交流闪络机理与泄漏电流特性研究:[D].长沙:长沙理工大学,2009
[19]贺博,林辉.高压绝缘子污闪过程特征量的分类和判别[J].高压电器,2006,42(3):172-176
[20] Richard C S, Benner C L, Butler-Purry K L, et al. Electrical behavior of contaminated distribution insulators exposed to natural wetting[J]. IEEE Trans. On Power Delivery, 2003,18(2):551-558
[21] Suda T. Frequency characteristics of leakage current waveforms of anartificially polluted suspension insulalor[J]. IEEE Transactions on Dielectrics and Electrical Insulation, 2001, 8(4): 705-710
[22]王珊珊,王建英,程旺宗.压缩传感理论在参数估计中应用[J].计算机工程与应用,2010,46 (12):120-122
[23]王蜀南.压缩传感在超宽带通信中的应用研究[D].吉林:吉林大学,2010
[24]傅迎华.可压缩传感重构算法与近似QR分解[J].计算机应用,2008,28(9): 2300-2302
[25]文继锋,刘沛.一种电能质量扰动检测的新方法[J].中国电机工程学报,2002,22(12):17-20
[26]杨奕,沈申生.小波包变换在电能质量扰动检测中的应用[J].高电压技术,2005, 32(7):116-119
[27]毕研秋.电力系统数据压缩的算法研究及通信网络仿真[D].山东:山东大学,2007
[28]肖湘宁.电能质量分析与控制[D].北京:中国电力出版社,2004:85-122
[29]邵君,尹忠科,王建英.基于FFT的MP信号稀疏分解算法的改进[J].西南交通大学学报,2006,41(4): 466-470
[30]王伟.Hilbert-Huang变换及其在非平稳信号分析中的应用研究[D].保定:华北电力大学,2008
[31] Trany T, Gan L. Fast compressive sampling with structurally random Matrices[J]. In Proceeding of the IEEE International Conference on Acoustics, Speech and Signal Processing. Washington DC,USA:IEEE, 2008, 3369-3372
[32]李卓凡,闫敬文.压缩感知及应用[J].微计算机应用,2010,31(3):13-16
[33]李小波.基于压缩感知的测量矩阵研究[D].北京:北京交通大学,2010
[34] Candes E, Terence T. Near optimal signal recovery from random projections universal encoding strategies[J]. IEEE Transaction on Information Theory, 2006,52(12): 5406-5425
[35] Candes E, Rom Berg J, Terengce T. Robust uncertainty principles: Excat signal reconstruction from highly incomplete frequency information[J]. IEEE Transactions on Information Theory, 2006, 52(2):489-50
[36] Tropp J, Gilbert A.Signal recovery from partial information via orthogonal matching pursuit[J].IEEE Trans Inform Theory, 2007, 53(12): 4566-4666
[37]张锐.基于压缩感知理论的图像压缩初步研究[J].电脑知识与技术,2010,6(4):958-959
[38]宁刚.基于压缩感知的信号重构算法研究[D].吉林:吉林大学,2010
[39] Thong T Do, Lu Gan, Nam Nguyen and Tran. Sparsity adaptive matching pursuit algorithm for practical compressed sensing. Asilomar Conference on Signals. Systems,and Computers, Pacific Grove, California, 2008, 10
[40] Needell D, Tropp J A. Iterative signal recovery from incomplete and inaccurate samples[J]. Apply compharmonical, 2009, 76(3):301-321
[41]李瑞玲.压缩传感在图像压缩恢复方面的技术研究[D].秦皇岛:燕山大学,2009
[42]方红,章权兵,韦穗.改进的后退型最优正交匹配追踪图像重构方法[J].华南理工大学学报,2008,36(8):23-27
[43] Krile John, A. Neuber, J. Dickens. Imaging of dielectric surface flashover in atmospheric conditions[J]. IEEE Transactions on Plasma Science, 2005, 33(21): 270-271
[44]王宏禹,邱天爽,陈喆．非平稳随机信号分析与处理[M].北京:国防工业出版社,2008
[45] Rauhut H, Schnass K V P. Compressed sensing and redundant dictionaries[J]. IEEE Transactions on Information Theory, 2008, 54(5): 2210-2219
[46]程文波,王华军.信号稀疏表示的研究及应用[J].西南石油大学学报(自然科学版),2008,30(5):148-151
[47]李天云,赵妍,季小慧等.HHT方法在电力系统故障信号分析中的应用[J].电工技术学报,2005,20(6):87-91
[48] Fiqueiredo M A T, Nowak R D, Wright S J. Gradient projection for sparse reconstruction:application to compressed sensing and other inverse problems[J]. IEEE J-STSP, 2007, 1(4): 586-598
[49] Suda T, Member S. Frequency characteristics of leakage current waveforms of a string of suspension insulators[J]. Transacations on Power Delivery, 2005, 20(1): 481-488
[50] Amarh F, Karady G G. Linear stochastic analysis of polluted insulator leakage current[J]. IEEE Transactions on power delivery, 2002, 17(4): 1063-1070
[51]杨存祥,万钰昊,庞小波.基于HHT的电力系统暂态复合扰动信号的提取分析与研究[J].电力系统保护与控制,2009,37(11):6-9
[52]聂永辉,高磊,唐威.Hilbert-Huang变换在电力系统暂态信号分析中的应用[J].电力系统及其自动化学报,2009,21(4):63-69
[53]谢启伟.经验模式分解算法分析和应用[D].合肥:中国科技大学,2008
[54]周小勇,叶银忠.故障信号检测的小波基选择方法.控制工程,2003,4(10):308-311
[55] Huang N E, et a1.The empirical mode decompsition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J]. Proceeding of the Royal Society of London Series A, 1998, 454:903-995
[56] Wu Z, Huang N E. A study of the characteristics of white noise using the empirical mode decomposition method[J]. Proceeding of the Royal Society of London Series A, 2004, 460(2046): 1597-1611
[57] Poisson O, Rioual P, Meunier. New signal Processing tools applied to power quality analysis[J]. IEEE Trans on Power Delivery, 1999, 14(2):561-566
[58] Leisk G , Hsu N, Huang N E. Application of the Hilbert-Huang Transform to machine tool condition/health monitoring.AIP conference proceeding, Brunswick, 2002, 615(l): 1711-1718
[59]刘稳坚,黄纯,等.经验模态分解及Hilbert谱分析在电压闪变检测中的应用[J].计算机测量与控制,2006,14(6):707-709
[60]李天云,李楠,等.基于EMD的Hilbert变换应用于暂态信号分析[J].电力系统自动化,2005, 29(4):49-52
[61] E. A. Feilat. Detection of Voltage Envelope Using Prony Analysis Hilbert Transform Method[J], IEEE Trans. Power De, 2006,21(4): 2091-2093
[2]关志成,毛颖科,王黎明.污秽绝缘子泄漏电流特性研究[J].高电压技术,2008,34(1):2-6
[3]李璟延,孙才新,司马文霞.染污绝缘子污闪全过程泄漏电流波形及谐波特性分析[J].高电压技术,2010,36(5):1101-1106
[4]惠阿丽,林辉,贺博.分形理论压缩和恢复高压绝缘子泄漏电流[J].高电压技术,2008,34(7):1395-1400
[5]孙秒平.电力系统故障录波数据压缩方法研究[J].电力自动化设备,2004,25(11):34-36
[6]黄险峰,钱路江.电力系统故障录波信号压缩[J].电力自动化设备,2001,21(11):18-21
[7]苗世洪,王少荣等.数据压缩技术在电力系统通信中的应用[J].电力自动化,1999,19(3):32-33
[8]石光明,刘丹华,高大化等.压缩感知理论及其研究进展[J].电子学报,2009,37(5):1070-1081
[9]李树涛,魏丹.压缩传感综述[J].自动化学报,2009,35(11): 1369-1377.
[10] Donoho D. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4): 1289-1306
[11]孙金凤.电力数据压缩传输及解压算法的研究与实现:[D].北京:北京化工大学,2008
[12]欧阳森,宋正湘,陈德桂.基于小波原理的电能质量检测数据实时压缩方法[J].电网技术, 2003,27(2):37-40
[13]李波,谢杰镇,王博亮.基于压缩传感理论的数据重建[J].计算机技术与发展,2009,19(5):23-25
[14]练秋生,郝鹏鹏.基于压缩传感的代数重建法德CT图像重建[J].光学技术,2009,35(3): 422-425
[15] Candes E, Wakin M B. An Introduction to Compressive Sampling [J].IEEE Signal Processing Magazine, 2008, 48(4): 21-30
[16] Candès E, Romberg J. Sparsity and incoherence in compressive sampling. Inverse Problems, 2007, 23(3): 969-985
[17]杜欣慧,戴云航,王志刚,宫改花.饱和受潮条件下的绝缘子泄漏电流特性[J].高电压技术,2007,33(9):6-9
[18]李毅.覆冰绝缘子交流闪络机理与泄漏电流特性研究:[D].长沙:长沙理工大学,2009
[19]贺博,林辉.高压绝缘子污闪过程特征量的分类和判别[J].高压电器,2006,42(3):172-176
[20] Richard C S, Benner C L, Butler-Purry K L, et al. Electrical behavior of contaminated distribution insulators exposed to natural wetting[J]. IEEE Trans. On Power Delivery, 2003,18(2):551-558
[21] Suda T. Frequency characteristics of leakage current waveforms of anartificially polluted suspension insulalor[J]. IEEE Transactions on Dielectrics and Electrical Insulation, 2001, 8(4): 705-710
[22]王珊珊,王建英,程旺宗.压缩传感理论在参数估计中应用[J].计算机工程与应用,2010,46 (12):120-122
[23]王蜀南.压缩传感在超宽带通信中的应用研究[D].吉林:吉林大学,2010
[24]傅迎华.可压缩传感重构算法与近似QR分解[J].计算机应用,2008,28(9): 2300-2302
[25]文继锋,刘沛.一种电能质量扰动检测的新方法[J].中国电机工程学报,2002,22(12):17-20
[26]杨奕,沈申生.小波包变换在电能质量扰动检测中的应用[J].高电压技术,2005, 32(7):116-119
[27]毕研秋.电力系统数据压缩的算法研究及通信网络仿真[D].山东:山东大学,2007
[28]肖湘宁.电能质量分析与控制[D].北京:中国电力出版社,2004:85-122
[29]邵君,尹忠科,王建英.基于FFT的MP信号稀疏分解算法的改进[J].西南交通大学学报,2006,41(4): 466-470
[30]王伟.Hilbert-Huang变换及其在非平稳信号分析中的应用研究[D].保定:华北电力大学,2008
[31] Trany T, Gan L. Fast compressive sampling with structurally random Matrices[J]. In Proceeding of the IEEE International Conference on Acoustics, Speech and Signal Processing. Washington DC,USA:IEEE, 2008, 3369-3372
[32]李卓凡,闫敬文.压缩感知及应用[J].微计算机应用,2010,31(3):13-16
[33]李小波.基于压缩感知的测量矩阵研究[D].北京:北京交通大学,2010
[34] Candes E, Terence T. Near optimal signal recovery from random projections universal encoding strategies[J]. IEEE Transaction on Information Theory, 2006,52(12): 5406-5425
[35] Candes E, Rom Berg J, Terengce T. Robust uncertainty principles: Excat signal reconstruction from highly incomplete frequency information[J]. IEEE Transactions on Information Theory, 2006, 52(2):489-50
[36] Tropp J, Gilbert A.Signal recovery from partial information via orthogonal matching pursuit[J].IEEE Trans Inform Theory, 2007, 53(12): 4566-4666
[37]张锐.基于压缩感知理论的图像压缩初步研究[J].电脑知识与技术,2010,6(4):958-959
[38]宁刚.基于压缩感知的信号重构算法研究[D].吉林:吉林大学,2010
[39] Thong T Do, Lu Gan, Nam Nguyen and Tran. Sparsity adaptive matching pursuit algorithm for practical compressed sensing. Asilomar Conference on Signals. Systems,and Computers, Pacific Grove, California, 2008, 10
[40] Needell D, Tropp J A. Iterative signal recovery from incomplete and inaccurate samples[J]. Apply compharmonical, 2009, 76(3):301-321
[41]李瑞玲.压缩传感在图像压缩恢复方面的技术研究[D].秦皇岛:燕山大学,2009
[42]方红,章权兵,韦穗.改进的后退型最优正交匹配追踪图像重构方法[J].华南理工大学学报,2008,36(8):23-27
[43] Krile John, A. Neuber, J. Dickens. Imaging of dielectric surface flashover in atmospheric conditions[J]. IEEE Transactions on Plasma Science, 2005, 33(21): 270-271
[44]王宏禹,邱天爽,陈喆．非平稳随机信号分析与处理[M].北京:国防工业出版社,2008
[45] Rauhut H, Schnass K V P. Compressed sensing and redundant dictionaries[J]. IEEE Transactions on Information Theory, 2008, 54(5): 2210-2219
[46]程文波,王华军.信号稀疏表示的研究及应用[J].西南石油大学学报(自然科学版),2008,30(5):148-151
[47]李天云,赵妍,季小慧等.HHT方法在电力系统故障信号分析中的应用[J].电工技术学报,2005,20(6):87-91
[48] Fiqueiredo M A T, Nowak R D, Wright S J. Gradient projection for sparse reconstruction:application to compressed sensing and other inverse problems[J]. IEEE J-STSP, 2007, 1(4): 586-598
[49] Suda T, Member S. Frequency characteristics of leakage current waveforms of a string of suspension insulators[J]. Transacations on Power Delivery, 2005, 20(1): 481-488
[50] Amarh F, Karady G G. Linear stochastic analysis of polluted insulator leakage current[J]. IEEE Transactions on power delivery, 2002, 17(4): 1063-1070
[51]杨存祥,万钰昊,庞小波.基于HHT的电力系统暂态复合扰动信号的提取分析与研究[J].电力系统保护与控制,2009,37(11):6-9
[52]聂永辉,高磊,唐威.Hilbert-Huang变换在电力系统暂态信号分析中的应用[J].电力系统及其自动化学报,2009,21(4):63-69
[53]谢启伟.经验模式分解算法分析和应用[D].合肥:中国科技大学,2008
[54]周小勇,叶银忠.故障信号检测的小波基选择方法.控制工程,2003,4(10):308-311
[55] Huang N E, et a1.The empirical mode decompsition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J]. Proceeding of the Royal Society of London Series A, 1998, 454:903-995
[56] Wu Z, Huang N E. A study of the characteristics of white noise using the empirical mode decomposition method[J]. Proceeding of the Royal Society of London Series A, 2004, 460(2046): 1597-1611
[57] Poisson O, Rioual P, Meunier. New signal Processing tools applied to power quality analysis[J]. IEEE Trans on Power Delivery, 1999, 14(2):561-566
[58] Leisk G , Hsu N, Huang N E. Application of the Hilbert-Huang Transform to machine tool condition/health monitoring.AIP conference proceeding, Brunswick, 2002, 615(l): 1711-1718
[59]刘稳坚,黄纯,等.经验模态分解及Hilbert谱分析在电压闪变检测中的应用[J].计算机测量与控制,2006,14(6):707-709
[60]李天云,李楠,等.基于EMD的Hilbert变换应用于暂态信号分析[J].电力系统自动化,2005, 29(4):49-52
[61] E. A. Feilat. Detection of Voltage Envelope Using Prony Analysis Hilbert Transform Method[J], IEEE Trans. Power De, 2006,21(4): 2091-2093
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