Research on Identification Model of Low Voltage Arc Characteristics Based on Wavelet Analysis

doi:10.16628/j.cnki.2095-8188.2021.06.002

Abstract

Abstract:

In order to solve the problems of accurate detection,quantitative characterization of arc state and the difficulty of recognizing arc voltage in a short time after zero,this paper proposed an AC arc recognition model based on different wavelet transform characteristics.Aiming at the time-frequency characteristics of series fault arc and switching arc,two identification models of orthogonal wavelet and dyadic wavelet were constructed and their effectiveness was verified.Introducing the concept of energy and combining dyadic wavelet to construct a wavelet energy spectrum model,the actual acquisition and analysis of the switching arc voltage waveform can realize the quantitative characterization of the switching arc state under different load conditions.The simulation and experimental results show that the AC arc recognition model based on wavelet analysis has a good effect in the recognition and extraction of arc feature,which provides a theoretical basis and new ideas for the diagnosis of arc fault and the on-line monitoring of switching arcs.

Key words: wavelet recognition model, series fault arc, switching arc, voltage waveform characteristics, energy spectrum

CLC Number:

TM572

SHAN Xiaojie, ZHENG Xin. Research on Identification Model of Low Voltage Arc Characteristics Based on Wavelet Analysis[J]. LOW VOLTAGE APPARATUS, 2021, 0(6): 7-14.

Figures/Tables 10

References 22

[1]	付光晶. 基于COMSOL的故障电弧仿真研究[D]. 上海:上海交通大学, 2018.
[2]	江润, 方艳东, 鲍光海, 等. 适用于低压串联故障电弧的Mayr改进模型[J]. 电器与能效管理技术, 2019(21):14-18.
[3]	杨建红, 张认成. 基于多信息融合的故障电弧保护系统的应用研究[J]. 高压电器, 2007(3):194-196.
[4]	李兴文. 低压电器空气电弧的近期研究进展[J]. 电器与能效管理技术, 2018(22):12-19.
[5]	付光晶, 张峰, 张士文. 基于COMSOL Multiphysics的交流故障电弧仿真研究[J]. 电器与能效管理技术, 2018(6):23-29.
[6]	CONG H X, LI Q M, XING J Y, et al. Critical length criterion and the arc chain model for calculating the arcing time of the secondary arc related to AC transmission lines[J]. Plasma Science and Technology, 2015, 17(6):475-480. doi: 10.1088/1009-0630/17/6/07
[7]	RONG M ZH, LI M, WU Y, et al. 3D MHD modeling of internal fault arc in a closed container[J]. IEEE Transactions on Power Delivery, 2017, 32(3):1220-1227. doi: 10.1109/TPWRD.2014.2375891
[8]	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.
[9]	CICHON A, BORUCKI T, BOCZAR T, et al. Characteristic of acoustic emission signals generated by electric arc in on load tap changer[C]// Proceedings of International Conference on Electrical Insulating Materials, 2011:437-439.
[10]	SAINI D, FLOYD S. An investigation of gas metal arc welding sound signature for on-line quality control[J]. Welding Journal, 1998, 17(4):172-179.
[11]	GAMMON T, LEE W J, ZHANG Z, et al. A review of commonly used DC arc models[J]// IEEE Trans-actions on Ind ustry Appl ication, 2015, 51(2):1398-1407.
[12]	陈昌垦. 基于希尔伯特-黄变换的串联型故障电弧特性研究[D]. 沈阳:辽宁工程技术大学, 2017.
[13]	CHEN CH K, GUO F Y, LIU Y L, et al. Recognition of series arc fault based on the Hilbert Huang transform[C]// The 61st IEEE Holm Conference on Electrical Contacts, 2015:324-330.
[14]	郭凤仪, 李坤, 陈昌垦, 等. 基于小波近似熵的串联电弧故障识别方法[J]. 电工技术学报, 2016, 31(24):164-172.
[15]	焦治杰, 李腾, 王莉娜, 等. 基于卷积神经网络的光伏系统直流串联电弧故障检测[J]. 电工电能新技术, 2019, 38(7):29-34.
[16]	郑昕. 低压配电系统短路电流快速分断控制实现的研究[D]. 福州:福州大学, 2007.
[17]	张进, 冯志鹏, 褚福磊. 滚动轴承故障特征的时间-小波能量谱提取方法[J]. 机械工程学报, 2011, 47(17):44-49.
[18]	苏晶晶, 许志红. 电弧故障保护电器动作特性测试分析系统[J]. 电力自动化设备, 2019, 39(10):194-200.
[19]	许志红. 电器理论基础[M]. 北京: 机械工业出版社, 2014.
[20]	徐豪, 郑昕. 基于LabVIEW的交流电弧分析系统设计[J]. 电器与能效管理技术, 2018(23):30-34.
[21]	高国强, 李成坤, 魏文赋, 等. 站内工况下降弓电弧特性的仿真研究[J]. 电器与能效管理技术, 2020(1):17-23.
[22]	周旭, 曹云东, 付思, 等. 电极旋转运动过程中直流空气电弧的动态分析[J]. 电器与能效管理技术, 2019(12):20-26.

P_k	db1	db2	db3	db4
P₀	1	0.683 012 7	0.470 467 2	0.325 803 4
P₁	1	1.183 012 7	1.141 116 9	1.010 945 7
P₂	—	0.316 987 3	0.650 365 0	0.892 201 4
P₃	—	-0.183 012 7	-0.190 934 4	-0.039 575 0
P₄	—	—	-0.120 832 2	-0.264 507 2
P₅	—	—	0.049 817 5	0.043 616 3
P₆	—	—	—	0.046 503 6
P₇	—	—	—	-0.014 987 0

参数	负载类型
参数	台灯	计算机	微波炉	空调
正常d4_MAX	0.02	0.02	0.04	0.10
启动d4_MAX	1.09	0.82	2.26	7.28
故障电弧d4_MAX	4.25	7.10	10.15	4.95