基于小波变换和奇异值分解的串联电弧故障检测方法

doi:10.19595/j.cnki.1000-6753.tces.170196

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Abstract Wavelet transform was a commonly used method to detect the arcing fault according to the change of the current signal in the circuit. However, it was not easy to distinguish the normal condition from arcing fault when simply using wavelet transform, and there was a lot of redundancy in the results. In order to solve this problem, a new detection method of series arcing fault which based on wavelet transform and singular value decomposition is proposed. An arc generator is used to generate series arcing fault, currents in normal condition and arcing fault are collected under multiple loads. Discrete wavelet transform is firstly used in the collected current signal, and the discrete wavelet coefficient sequence is obtained. Then, based on singular value decomposition of characteristic matrix, the characteristic parameters of current signal are defined and used as the basis of the series arcing fault detection. The experimental results show that it is easy to distinguish the characteristic parameters and there is no cross under normal condition and series arcing fault, thus it is easy to determine the threshold value. The accuracy of the series arcing fault detection is high, and the redundancy of the wavelet transform is greatly compressed.

Key words： Arcing fault wavelet transform singular value decomposition characteristic parameters detection

Received: 27 February 2017 Published: 14 September 2017

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TM501+.2

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	Lu Qiwei
	Wang Tao
	Li Zongrui
	Wang Cong

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Lu Qiwei,Wang Tao,Li Zongrui等. Detection Method of Series Arcing Fault Based on Wavelet Transform and Singular Value Decomposition[J]. Transactions of China Electrotechnical Society, 2017, 32(17): 208-217.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.170196 OR https://dgjsxb.ces-transaction.com/EN/Y2017/V32/I17/208

[1] 全国消防标准化技术委员会火灾探测与报警分技术委员会. GB14287.4—2014, 电气火灾监控系统第4部分: 故障电弧探测器[S]. 北京: 中国标准出版社, 2014.
[2] 全国低压电器标准化技术委员会. GB/T31143—2014, 电弧故障保护电器(AFDD)的一般要求[S]. 北京: 中国标准出版社, 2014.
[3] Gregory G D, Scott G W. The arc-fault circuit interrupter: an emerging product[J]. IEEE Transactions on Industry Applications, 1998, 34(5): 928-933.
[4] Noon R K. Engineering analysis of fires and explosions[J]. U. c. davis J. juv. l. & Poly, 1995, 134(2): 347-52.
[5] UL1699, Arc-fault circuit-interrupts[S]. America, 1999.
[6] 年培新, 罗时璜, 董葆生, 等. 低压配电领域中的故障电弧防护[J]. 低压电器, 2000(1): 22-26.
Nian Peixin, Luo Shihuang, Dong Baosheng, et al. Protection of fault arc in the fields of low voltage power distribution[J]. Low Voltage Apparatus, 2000(1): 22-26.
[7] 陈德桂. 低压电弧故障断路器——一种新型低压保护电器[J]. 低压电器, 2007(3): 7-9.
Chen Degui. The arc-fault circuit interrupter—a new low voltage protection apparatus[J]. Low Voltage Apparatus, 2007(3): 7-9.
[8] Restrepo C E. Arc fault detection and discrimination methods[C]//The 53rd IEEE Holm Conference on Electrical Contacts, Pittsburgh, USA, 2007.
[9] 蓝会立, 张认成. 基于小波分析的故障电弧伴生弧声特征提取[J]. 电力系统及其自动化学报, 2008, 20(4): 57-62.
Lan Huili, Zhang Rencheng. Study on the feature extraction of fault arc sound signal based on wavelet analysis[J]. Proceedings of the CSU-EPSA, 2008, 20(4): 57-62.
[10] 卢其威, 巫海东, 王肃珂, 等. 基于差值均方根法的故障电弧检测的研究[J]. 低压电器, 2013(1): 6-10.
Lu Qiwei, Wu Haidong, Wang Suke, et al. Research on arc-fault detection based on difference-root mean square method[J]. Low Voltage Apparatus, 2013(1): 6-10.
[11] 德州仪器公司. 检测电弧故障的设备和方法: 中国, 200510103736. 2[P]. 2006-03-15.
[12] 杨凯, 张认成, 杨建红, 等. 基于分形维数和支持向量机的串联电弧故障诊断方法[J]. 电工技术学报, 2016, 31(2): 70-76.
Yang Kai, Zhang Rencheng, Yang Jianhong, et al. Series arc fault diagnostic method based on fractal dimension and support vector machine[J]. Transactions of China Electrotechnical Society, 2016, 31(2): 70-76.
[13] 马少华, 鲍洁秋, 蔡志远, 等. 基于信息维数和零休时间的电弧故障识别方法[J]. 中国电机工程学报, 2016, 36(9): 2572-2579.
Ma Shaohua, Bao Jieqiu, Cai Zhiyuan, et al. A novel arc fault identification method based on information dimension and current zero[J]. Proceedings of the CSEE, 2016, 36(9): 2572-2579.
[14] Gregory G D, Wong K, Dvorak R F. More about arc-fault circuit interrupters[J]. IEEE Transactions on Industry Applications, 2004, 40(4): 1006-1011.
[15] 刘晓明, 徐叶飞, 刘婷, 等. 基于电流信号短时过零率的电弧故障检测[J]. 电工技术学报, 2015, 30(13): 125-133.
Liu Xiaoming, Xu Yefei, Liu Ting, et al. The arc fault detection based on the current signal short time zero crossing rate[J]. Transactions of China Electrotechnical Society, 2015, 30(13): 125-133.
[16] Georgijevic N, Jankovic M, Srdic S, et al. The detection of series arc-fault in photovoltaic systems based on the arc current entropy[J]. IEEE Transactions on Power Electronics, 2015, 31(8): 5917-5930.
[17] 孙鹏, 郑志成, 闫荣妮, 等. 采用小波熵的串联型故障电弧检测方法[J]. 中国电机工程学报, 2010, 30(增刊1): 232-235.
Sun Peng, Zheng Zhicheng, Yan Rongni, et al. Detection method of arc fault in series with wavelet entropy[J]. Proceedings of the CSEE, 2010, 30(S1): 232-235.
[18] 缪希仁, 郭银婷, 唐金城, 等. 负载端电弧故障电压检测与形态小波辨识[J]. 电工技术学报, 2014, 29(3): 237-244.
Miao Xiren, Guo Yinting, Tang Jincheng, et al. Load side arc fault voltage detection and identification with morphological wavelet[J]. Transactions of China Electrotechnical Society, 2014, 29(3): 237-244.
[19] 刘晓明, 赵洋, 曹云东, 等. 基于小波变换的交流系统串联电弧故障诊断[J]. 电工技术学报, 2014, 29(1): 10-15.
Liu Xiaoming, Zhao Yang, Cao Yundong, et al. Series arc fault diagnosis based on wavelet transform in AC system[J]. Transactions of China Electrotechnical Society, 2014, 29(1): 10-15.
[20] 张士文, 张峰, 王子骏, 等. 一种基于小波变换能量与神经网络结合的串联型故障电弧辨识方法[J]. 电工技术学报, 2014, 29(6): 290-295.
Zhang Shiwen, Zhang Feng, Wang Zijun, et al. Series arc fault identification method based on energy produced by wavelet transformation and neural network[J]. Transactions of China Electrotechnical Society, 2014, 29(6): 290-295.
[21] 张冠英, 张晓亮, 刘华, 等. 低压系统串联故障电弧在线检测方法[J]. 电工技术学报, 2016, 31(8): 109-115.
Zhang Guanying, Zhang Xiaoliang, Liu Hua, et al. Online detection method for series arcing fault in low voltage system[J]. Transactions of China Electrotechnical Society, 2016, 31(8): 109-115.
[22] 刘润涛, 孙中喜, 倪金霞, 等. 基于奇异值分解的小波域灰度数字水印算法[J]. 哈尔滨工业大学学报, 2009, 41(11): 193-196.
Liu Runtao, Sun Zhongxi, Ni Jinxia, et al. Gray-level digital watermarking algorithm based on singular value decomposition in wavelet domain[J]. Journal of Harbin Institute of Technology, 2009, 41(11): 193-196.
[23] 唐炬, 李伟, 欧阳有鹏. 采用小波变换奇异值分解方法的局部放电模式识别[J]. 高电压技术, 2010, 36(7): 1686-1690.
Tang Ju, Li Wei, Ouyang Youpeng. Partial discharge pattern recognition using wavelet transform and singular value decomposition[J]. High Voltage Engineering, 2010, 36(7): 1686-1690.
[24] 李国宾, 关德林, 李廷举. 基于小波包变换和奇异值分解的柴油机振动信号特征提取研究[J]. 振动与冲击, 2011, 30(8): 149-152.
Li Guobin, Guan Delin, Li Tingju. Feature extraction of diesel engine vibration signal based on waveletpacket transform and singular value decomposition[J]. Journal of Vibration and Shock, 2011, 30(8): 149-152.
[25] 赵学智, 叶邦彦, 陈统坚. 基于小波-奇异值分解差分谱的弱故障特征提取办法[J]. 机械工程学报, 2012, 48(7): 37-47.
Zhao Xuezhi, Ye Bangyan, ChenTongjian. Extraction method of faint fault feature based on wavelet-SVD difference spectrum[J]. Journal of Mechanical Engineering, 2012, 48(7): 37-47.
[26] 周伟. 基于MATLAB的小波分析应用[M]. 西安: 西安电子科技大学出版社, 2010.
[27] 冯象初, 甘小冰, 宋国乡. 数值泛函与小波理论[M]. 西安: 西安电子科技大学出版社, 2003.
[28] 戴华. 矩阵论[M]. 北京: 科学出版社, 2001.