Research of Arc Fault Detecting Based on Hilbert-Huang Transformation and Support Vector Machine

doi:10.11985/2017.07.005

Abstract

Abstract:

With the increasing consumption of electric equipment, the occurring rates of electrical fires also rise in a high speed. This paper proposes a method combine Hilbert-Huang Transformation (HHT) and Support Vector Machine (SVM). We adopt the HHT to decompose the current waveform in different kinds of loads, and analyze the Intrinsic Mode Function from time domain and frequency domain. The SVM learns characteristic value in different loads, thus identification of normal condition and arc fault condition can be done in an adaptive way. This paper takes five kinds of common loads (dimmer, drier with high power, drier with low power, fan, heater, etc.) for the research object to verify the validity of the proposed method.

Key words: Arc detecting, arc fault circuit interrupter, Hilbert-Huang transformation, support vector machine

CLC Number:

TM711

Zhang Zhan,Yang Guang,Zhang Feng. Research of Arc Fault Detecting Based on Hilbert-Huang Transformation and Support Vector Machine[J]. Journal of Electrical Engineering, 2017, 12(7): 26-32.

Figures/Tables 6

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References 10

[1]	UL1699, UL Standard for Safety for Arc Fault Circuit Interrupters[S]. 2nd Edition, 2006.
[2]	Schavemaker P H, Van der Slui L . An improved mayr-type arc model based on current-zero measurements [circuit breakers][J]. IEEE Transactions on Power Delivery, 2000,15(2):580-584.
[3]	Gregory G D, Scott G W . The arc-fault circuit interrupter, an emerging product[C]. IEEE Industrial and Commercial Power Systems Technical Conference, 1998: 48-55.
[4]	Tom Potter, 颜培荣 . AFCI 技术在家用空调电源插头中的应用[J]. 电器, 2004(9):36-37.
	Potter T, Yan Peirong . Application of AFCI in Power plug for household air conditioner[J]. China Appli ance, 2004, ( 09):36-37.
[5]	Carvou E, Ben Jemaa N. Electrical arc characterization for ac-arc fault applications[C]. Proceedings of the 55th IEEE Holm Conference on Electrical Contacts, Vancouver, 2009: 21-26.
[6]	钱昌松, 刘代志, 刘志刚 , 等. 基于递归高通滤波的经验模态分解及其在地震信号分析中的应用[J]. 地球物理学报, 2010,53(5):1215-1225. doi: 10.3969/j.issn.0001-5733.2010.05.024
	Qian Changsong, Liu Daizhi, Liu Zhigang , et al. EMD based on recursive high-pass filter and its application on seismic signal analysis[J]. Chinese Journal of Geophysics, 2010,53(5):1215-1225. doi: 10.3969/j.issn.0001-5733.2010.05.024
[7]	Rilling G, Flandrin P, Gonçalvés P. On empirical mode decomposition and its algorithms[C]. IEEE-EURASIP Workshop on Nonlinear Signal and Image Processing NSIP, 2003,3:8-11.
[8]	张冠英, 曹旺林, 李长伟 , 等. 低压故障电弧检测与周期辨识[J]. 电气应用, 2016,35(23):81-85.
[9]	万畅 . 齿轮箱轴承故障诊断的时频分析方法初步探讨[D]. 兰州:兰州理工大学, 2007.
[10]	董红生 . 心电波形检测与心率变异性分析方法研究[D]. 兰州:兰州理工大学, 2012.

序号	负载	功率/W	类型
1	电水壶	1 000	阻性
2	电吹风高档	1 000	近似阻性
3	电吹风低档	600	开关电源
4	电风扇（电容起动型）	50	容性
5	调光灯	1 000	开关电源

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