Arc fault diagnosis method based on chaos and fractal theories

被引：0

作者：

Su J.-J. ^{[1
,2
]}

Xu Z.-H. ^{[1
]}

机构：

[1] School of Electrical Engineering and Automation, Fuzhou University, Fuzhou

[2] College of Computer and Control Engineering, Minjiang University, Fuzhou

来源：

Dianji yu Kongzhi Xuebao/Electric Machines and Control | 2021年 / 25卷 / 03期

关键词：

Arc fault; Chaos and fractal characteristics; Fault arc diagnosis; Probabilistic neural network; Reconstructing phase space; Space domain feature;

D O I：

10.15938/j.emc.2021.03.014

中图分类号：

学科分类号：

摘要：

Based on the chaos fractal theories, the characteristics and the internal evolution of arc fault were analyzed, and an arc fault diagnosis method was put forward. The chaos and fractal characteristics of arc were qualitatively and quantitatively analyzed by using the reconstruction phase space theory and chaotic and fractal feature parameters, such as box dimension, correlation dimension and Lyapunov index. Then the spatial domain eigenvectors and the diagnosis model of arc fault were constructed. The chaos and fractal characteristics of current pre- and post-arc fault were analyzed to verify validity for low-voltage power systems with air compressors and switching power supplies. Experimental results show that the fractal structures and the characteristic parameters of chaotic fractal of current are different in the change of running state and load.Different evolution trend between normal current and arc current, and the chaos and fractal characteristic parameters show different rules. The accuracy of arc diagnosis model based on this feature is more than 90%. Meanwhile, the load identification rate is more than 90% under normal operation. © 2021, Harbin University of Science and Technology Publication. All right reserved.

引用

页码：125 / 133

页数：8

共 19 条

[1]

GUO Fengyi, LI Kun, CHEN Changken, Et al., Series arc fault identification method based on wavelet approximate entropy, Transactions of China Electrotechnical Society, 31, 24, (2016)

[2]

LIU Xiaoming, XU Yefei, LIU Ting, Et al., The arc fault detection based on the current signal short time zero crossing rate, Transactions of China Electrotechnical Society, 30, 13, (2015)

[3]

ZHAO Jingbo, TANG Yongwei, ZHANG Lei, Improved wavelet transform algorithm of anti spectrum aliasing based on adding odd extraction before a node, Electric Machines and Control, 20, 2, (2016)

[4]

ZENG Ke, XING Lidong, ZHANG Yaojia, Et al., Characteristics analysis of AC arc fault in time and frequency domain, 2017 Prognostics and System Health Management Conference, (2017)

[5]

SU Jingjing, XU Zhihong, Diagnosis method of multi-variable criterion based on EMD and PNN for arc fault diagnosis, Electric Power Automation Equipment, 39, 4, (2019)

[6]

GHORUI S, SAHASRABUDHE S H, MURTHY P S S, Et al., Experimental evidence of chaotic behavior in atmospheric pressure arc discharge, IEEE Transactions on Plasma Science, 28, 1, (2000)

[7]

LIU Xiaoming, HAN Ying, WANG Erzhi, Et al., Chaos of arc model for SF6 circuit breaker based on coupled multiple-physical field simulation, Transactions of China Electrotechnical Society, 28, 1, (2013)

[8]

YANG Jianhong, ZHANG Rencheng, FANG Huaiying, Et al., Early detecting of fault arcs using Lyapunov exponents, Advanced Technology of Electronic Engineering and Energy, 27, 2, (2008)

[9]

LAN Huili, ZHANG Rencheng, Study on key technology of DSP-based forecast & early warning system of faults arc, Computer Measurement & Control, 19, 2, (2011)

[10]

HU Yu, CHEN Tao, Phase-space reconstruction technology of chaotic attractor based on C-C method, Journal of Electronic Measurement and Instrument, 26, 5, (2012)

← 1 2 →