基于S变换局部奇异值分解的过电压特征提取

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摘要针对实测过电压信号波形不规则、不易识别问题, 根据矩阵奇异值的特点, 提出了一种采用S变换和局部奇异值分解的过电压特征提取方法。首先通过S变换得到过电压发生时零序电压的时频模值矩阵, 然后对矩阵进行划分, 计算各个子矩阵的最大奇异值, 利用最大奇异值在不同频带以及整个时频空间的分布差异来构造过电压特征量。对变电站实测的感应雷过电压等五种过电压信号的计算表明, 所提取的特征量维数低, 对过电压信号随机扰动具有相对稳定性, 能提取出过电压的本质特征。

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	杜林
	戴斌
	陆国俊
	孙才新
	王有元

关键词 ：过电压, 特征提取, S变换, 奇异值分解, 局部奇异值特征

Abstract：A new algorithm to extract overvoltage features as identification parameters is proposed with consideration of the irregularity of field acquired overvoltage. According to this algorithm, the time-frequency matrix of zero sequence voltage, which is obtained by S transform, is divided into submatrixs. Then, by calculating the maximum singular value of each submatrixs, the overvoltage features is constructed by utilizing the singular value difference between different frequency band or the whole time-frequency space. The test results of field acquired overvoltage data, such as lightning induced overvoltage and so on, indicate that the feature extraction algorithm based on S transform and local singular value decomposition is effective.

Key words： Overvoltage extract feature S transform singular value decomposition local singular value feature

收稿日期: 2009-04-08 出版日期: 2014-03-04

PACS:

TM835.1

基金资助:国家重点基础研究发展计划(973计划)(2009CB724504)和重庆市科技攻关计划(CSTC2010AC3051)资助项目

作者简介: 杜林男, 1971年生, 博士, 教授, 主要从事高压电气设备绝缘在线监测及故障诊断研究。戴斌男, 1984年生, 硕士研究生, 从事过电压在线监测及模式识别研究。

引用本文:

杜林, 戴斌, 陆国俊, 孙才新, 王有元. 基于S变换局部奇异值分解的过电压特征提取[J]. 电工技术学报, 2010, 25(12): 147-153. Du Lin, Dai Bin, Lu Guojun, Sun Caixin, Wang Youyuan. Overvoltage Features Extraction Based on S Transform and Local Singular Value Decomposition. Transactions of China Electrotechnical Society, 2010, 25(12): 147-153.

链接本文:

http://dgjsxb.ces-transaction.com/CN/Y2010/V25/I12/147

[1] 孙才新, 司马文霞, 赵杰, 等. 特高压输电系统的过电压问题[J]. 电力自动化设备, 2005, 25(9): 5-9.
Sun Caixin, Sima Wenxia, Zhao Jie, et al. Overvoltage in UHV transmission system[J]. Electric Power Automation Equipment, 2005, 25(9): 5-9.
[2] 兰海涛, 司马文霞, 姚陈果, 等. 高压电网过电压在线监测数据采集方法研究[J]. 高电压技术, 2007, 33(3): 79-83.
Lan Haitao, Sima Wenxia, Yao Chenguo, et al. Study on data acquisition of overvoltage online monitoring system of high voltage power grid[J]. High Voltage Engineering, 2007, 33 (3): 79-83.
[3] 姚陈果, 孙才新, 米彦, 等．配电网过电压在线监测系统的设计与实现[J]. 电力系统自动化, 2004, 28(9): 74-76.
Yao Chenguo, Sun Caixin, Mi Yan, et al. An on-line monitoring system for over-voltage of distribution networks[J]. Automation of Electric Power Systems, 2004, 28(9): 74-76.
[4] Santoso S, Powers E J, Grady W M, et al. Power quality assessment via wavelet transform analysis[J]. IEEE Transactions on Power Delivery, 1996, 11(2): 924-930.
[5] Wilkinson W A, Cox M D. Discrete wavelet analysis of power system transients[J]. IEEE Transactions on Power Systems, 1996, 11(4): 2038-2044.
[6] 潘翀, 陈伟根, 云玉新, 等. 基于遗传算法进化小波神经网络的电力变压器故障诊断[J]. 电力系统自动化, 2007, 31(13):88-92.
Pan Chong, Chen Weigen, Yun Yuxin, et al. Fault diagnose of power transformer based on genetic algorithm evolving wavelet neural networks[J]. Automation of Electric Power Systems, 2007, 31(13): 88-92.
[7] Santoso S, Powers E J, Grady W M．Power quality disturbance identification using wavelet transforms and artificial neural networks[C]．Proceedings of IEEE ICHQP Ⅶ, Lasvegas, 1996: 615-618．
[8] 占勇, 程浩忠, 丁屹峰, 等．基于S变换的电能质量扰动支持向量机分类识别[J]. 中国电机工程学报, 2005, 25(4): 51-56.
Zhan Yong, Cheng Haozhong, Ding Yifeng, et al. S-transform-based classification of power quality disturbance signals by support vector machine[J]. Proceedings of the CSEE, 2005, 25(4): 51-56.
[9] Stockwell R G, Mansinha L, Lowe R P. Localization of the complex spectrum: the S transform[J].IEEE Transactions on Signal Processing, 1996, 44(4): 998-1001.
[10] Dash P L, Panigrahi B K, Panda G. Power quality analysis using S-transform[J]. IEEE Transactions on Power Delivery, 2003, 18(2): 406-411.
[11] Lee I W C, Dash P L. S-transform-based intelligent system for classification of power quality disturbance signals[J]. IEEE Transactions on Power Delivery, 2003, 50(4): 800-805.
[12] Chilunkuri M V, Dash P L. Multiresolution S- transform-based fuzzy recognition system for power quality events[J]. IEEE Transactions on Power Delivery, 2004, 19(1): 323-329.
[13] Dash P L, Panigrahi B K, Sahoo D K, et al. Power quality disturbance data compression, detection, and classification using integrated spline wavelet and S-transform[J]. IEEE Transactions on Power Delivery, 2003, 18(2): 595-600.
[14] 杨洪耕, 刘守亮, 肖先勇, 等. 基于S变换的电压凹陷分类专家系统[J]. 中国电机工程学报, 2007, 27(1): 98-104.
Yang Honggeng, Liu Shouliang, Xiao Xianyong, et al. S-transform-based expert system for classification of voltage dips[J]. Proceedings of the CSEE, 2007, 27(1): 98-104.
[15] 张贤达. 矩阵分析与应用[M]. 北京: 清华大学出版社, 2004.
[16] 张峰, 梁军, 张利, 等. 奇异值分解理论和小波变换结合的行波信号奇异点检测[J]. 电力系统自动化, 2008, 32(20)：57-60.
Zhang Feng, Liang Jun, Zhang Li, et al. Travel wave signal processing for singularity detection based on singular value decomposition and wavelet transform [J]. Automation of Electric Power Systems, 2008, 32(20): 57-60.
[17] 梁霖, 徐光华, 刘弹, 等. 小波-奇异值分解在异步电机转子故障特征提取中的应用[J]. 中国电机工程学报, 2005, 25(19) : 111-115.
Liang Lin, Xu Guanghua, Liu Dan, et al. A feature extraction method of rotor faults of induction motor based on continuous wavelet transform and singular decomposition[J]. Proceedings of the CSEE, 2005, 25(19): 111-115.
[18] 魏小鹏, 于万波, 金一粟. 奇异值方法用于汽车车型识别[J].中国图像图形学报, 2003, 8A(1): 47-50.
Wei Xiaopeng, Yu Wanbo, Jin Yishu. Car shape recognition based on matrix singular value[J]. Journals of Images and Graphics, 2003, 8A(1): 47-50.
[19] 洪子全, 杨静宇. 基于奇异值特征和统计模型的人像识别算法[J]. 计算机研究与发展, 1994, 31(3): 60-65.
Hong Ziquan, Yang Jingyu. Human face recognition algorithm based on singular value features and statistical models[J]. Computer Research and Development, 1994, 31(3): 60-65.
[20] 闫荣华, 彭进业, 李岩, 等. 基于小波域奇异值分解的人脸识别方法[J]. 计算机工程, 2007, 33(4): 212-217.
Yan Ronghua, Peng Jinye, Li Yan, et al. Method of face recognition based on singular value decomposition in the wavelet domain[J]. Computer Engineering 2007, 33(4): 212-217.
[21] 甘俊英, 张有为. 一种基于奇异值特征的神经网络人脸识别新途径[J]. 电子学报, 2004, 32(1)： 170-173.
Gan Junying, Zhang Youwei. A new approach for face recognition based on singular value features and neural networks[J]. Actar Electronic Sinica, 2004, 32(1)：170-173.
[22] 杜林, 刘伟明, 王有元, 等. 基于CPLD的电网过电压变频数据采集卡设计[J]. 高电压技术, 2008, 34(8): 1589-1593.
Du Lin, Liu Weiming, Wang Youyuan, et al. Data acquisition card with variable sampling speed for monitoring overvoltage based on CPLD[J]. High Voltage Engineering, 2008, 34(8): 1589-1593.
[23] Tian Y, Tang T, Wang Y H, et al. Do singular value contain adequate information for face recognition[J]. Pattern Recognition, 2003, 36(6): 649-655.