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.
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页码：125 / 133
页数：8
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