局部二值模式直方图匹配的串联故障电弧检测及选线

doi:10.19595/j.cnki.1000-6753.tces.190118

Abstract
Figure/Table
References
Related Citation (15)

Download: PDF (59231 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract Series arc fault is one of the main causes of electric fire. Effective detection of series arc fault is of great significance in preventing electric fire. The series arc fault experiment of multi-load circuit was carried out with six kinds of household loads. A new arc fault detection and line selection method was proposed. Firstly, fractional Fourier transform of main loop current signal was used to construct an arc fault image matrix. The matrix can fully reflect the change process of current signal from time domain to frequency domain. Secondly, the local binary pattern (LBP) was used to describe the local texture features of the image matrix. And the gray distribution histogram of the LBP image was obtained statistically. The LBP histogram data under different experimental conditions were used to establish a database. Finally, the maximum correlation coefficient matching criterion was used to realize arc fault detection and line selection. The test results show that the proposed method can realize arc fault detection and line selection in multi-load circuit, and the accuracy of detection and line selection is higher than 90%.

Key words： Arc fault fractional Fourier transform texture feature local binary pattern database matching fault line selection

Received: 28 January 2019 Published: 24 April 2020

PACS:

TM501+.2

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	Guo Fengyi
	Gao Hongxin
	Tang Aixia
	Wang Zhiyong

Cite this article:

Guo Fengyi,Gao Hongxin,Tang Aixia等. Series Arc Fault Detection and Line Selection Based on Local Binary Pattern Histogram Matching[J]. Transactions of China Electrotechnical Society, 2020, 35(8): 1653-1661.

URL:

https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.190118 OR https://dgjsxb.ces-transaction.com/EN/Y2020/V35/I8/1653

[1] Panetta S.Design of arc flash protection systemusing solid state switch, photo detection, with parallel impedance[C]//2013 IEEE IAS Electrical Safety Workshop, Dallas, TX, USA, 2013: 211-213.
[2] 刘柱揆, 丁心志, 陈福明, 等. 开关柜电弧故障及其测量方法研究[J]. 电网技术, 2017, 41(4): 1345-1349.
Liu Zhukui, Ding Xinzhi, Chen Fuming, et al.Research on switchgear arc discharge fault and its measurement[J]. Power System Technology, 2017, 41(4): 1345-1349.
[3] 张杰, 马宏伟. 矿用高压开关柜的弧光监测与保护系统[J]. 电力系统保护与控制, 2013, 41(11): 141-145.
Zhang Jie, Ma Hongwei.Online monitoring and protection system for the arc fault of the mining high- voltage switchgear[J]. Power System Protection and Control, 2013, 41(11): 141-145.
[4] 李奎, 陈照, 张洋子, 等. 基于聚类分析和电磁辐射信号的电弧故障识别[J]. 电机与控制学报, 2018, 22(5): 94-101.
Li Kui, Chen Zhao, Zhang Yangzi, et al.Arc fault detection based on cluster analysis and electro- magnetic radiation[J]. Electric Machines and Control, 2018, 22(5): 94-101.
[5] Liu Yuwei, Wu C J, Wang Y C.Detection of serial arc fault on low-voltage indoor power lines by using radial basis function neural network[J]. International Journal of Electrical Power and Energy Systems, 2016, 83: 149-157.
[6] Siegel J E, Pratt S, Sun Yongbin, et al.Real-time deep neural networks for internet-enabled arc-fault detection[J]. Engineering Applications of Artificial Intelligence, 2018, 74: 35-42.
[7] Artale G, Cataliotti A, Cosentino V, et al.Arc fault detection method based on CZT low-frequency harmonic current analysis[J]. IEEE Transactions on Instrumentation and Measurement, 2017, 66(5): 888-896.
[8] Gao Hongxin, Wang Xili, Nguyen T, et al.Research on feature of series arc fault based on improved SVD[C]//2017 IEEE Holm Conference on Electrical Contacts, Denver, USA, 2017: 325-331.
[9] 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 Electro- technical Society, 2016, 31(8): 109-115.
[10] 郭凤仪, 高洪鑫, 王智勇, 等. 基于ST-SVD-PCA的串联故障电弧特征提取方法[J]. 煤炭学报, 2018, 43(3): 888-896.
Guo Fengyi, Gao Hongxin, Wang Zhiyong, et al.Feature extraction method of series fault arc based on ST-SVD-PCA[J]. Journal of China Coal Society, 2018, 43(3): 888-896.
[11] 占友雄, 张认成, 杨建红, 等. 基于Camberra距离的串联电弧故障诊断方法[J]. 电力系统保护与控制, 2014, 42(12): 30-36.
Zhan Youxiong, Zhang Rencheng, Yang Jianhong, et al.Series arcing fault diagnosis based on Camberra distance[J]. Power System Protection and Control, 2014, 42(12): 30-36.
[12] 郭凤仪, 邓勇, 王智勇, 等. 基于灰度-梯度共生矩阵的串联故障电弧特征[J]. 电工技术学报, 2018, 33(1): 71-81.
Guo Fengyi, Deng Yong, Wang Zhiyong, et al.Series arc fault characteristics based on gray level-gradient co-occurrence matrix[J]. Transactions of China Elec- trotechnical Society, 2018, 33(1): 71-81.
[13] 管红立, 王博文, 赵智忠. 基于相空间重构和 Lyapunov指数电弧电流混沌特性分析[J]. 电工电能新技术, 2017, 36(3): 35-40.
Guan Hongli, Wang Bowen, Zhao Zhizhong.Chaotic characteristic analysis of arc current based on phase space reconstruction and Lyapunov exponent[J]. Advanced Technology of Electrical Engineering and Energy, 2017, 36(3): 35-40.
[14] 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): 70-77.
Yang Kai, Zhang Rencheng, Yang Jianhong, et al.Series arc fault diagnostic method based on fractal dimension and support vector machine[J]. Transa- ctions of China Electrotechnical Society, 2016, 31(2): 70-77.
[15] 文成林, 吕菲亚, 包哲静, 等. 基于数据驱动的微小故障诊断方法综述[J]. 自动化学报, 2016, 42(9): 1285-1299.
Wen Chenglin, Lü Feiya, Bao Zhejing, et al.A review of data driven-based incipient fault diag- nosis[J]. Acta Automatica Sinica, 2016, 42(9): 1285-1299.
[16] 罗洁思, 于德介, 陈向民. 线调频小波路径追踪算法和FrFT相结合的升降速齿轮故障诊断方法[J]. 机械工程学报, 2012, 48(7): 56-61.
Luo Jiesi, Yu Dejie, Chen Xiangmin.Gear fault diagnosis under the speed-up and speed-down process based on the chirplet path pursuit algorithm and FrFT[J]. Journal of Mechanical Engineering, 2012, 48(7): 56-61.
[17] Wu Jinmin, Lu Mingfeng, Tao Ran, et al.Improved FRFT-based method for estimating the physical parameters from Newton’s rings[J]. Optics and Lasers in Engineering, 2017, 91: 178-186.
[18] 王小玉, 张亚洲, 陈德运. 基于多块局部二值模式特征和人眼定位的人脸检测[J]. 仪器仪表学报, 2014, 35(12): 2739-2745.
Wang Xiaoyu, Zhang Yazhou, Chen Deyun.Face detection based on MB-LBP and eye tracking[J]. Chinese Journal of Scientific Instrument, 2014, 35(12): 2739-2745.
[19] Lezama J, Schweitzer P, Weber S, et al.Arcing detection at home system using correlation analysis[C]// The 27th International Conference on Electrical Contacts, Dresden, Germany, 2014: 587-592.
[20] 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): 10-17.
Liu Xiaoming, Zhao Yang, Cao Yundong, et al.Series arc fault diagnosis based on wavelet transform in AC system[J]. Transactions of China Electro- technical Society, 2014, 29(1): 10-17.
[21] 马少华, 鲍洁秋, 蔡致远, 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.
[22] 全国消防标准技术委员会火灾与预警分技术委员会. GB 14287—2014 电气火灾监控系统[S]. 北京:中国标准出版社, 2014.
[23] 王智勇, 郭凤仪, 冯晓丽, 等. 基于电流信号特征的弓网电弧识别方法[J]. 电工技术学报, 2018, 33(1): 82-91.
Wang Zhiyong, Guo Fengyi, Feng Xiaoli, et al.Recognition method of pantograph arc based on current signal characteristics[J]. Transactions of China Electrotechnical Society, 2018, 33(1): 82-91.