Detection of DC Series Arc Fault Based on VMD and ELM

With the increase of domestic electrical equipment, the incidence of electrical fires has also increased, and research on fault arc detection has become a hot topic today. In this paper, a method combining variational mode decomposition (VMD), and extreme learning machine (ELM) is proposed to detect...

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Published inJournal of physics. Conference series Vol. 1486; no. 6; pp. 62037 - 62042
Main Authors Ma, Tao, Tian, Ersheng, Liu, Zhenxing, Liu, Shuxin, Guo, Tianhong, Wang, Taowei, Fu, Long
Format Journal Article
LanguageEnglish
Published Bristol IOP Publishing 01.04.2020
Subjects
Artificial neural networks
Decomposition
Electric equipment
Electrical fires
Fault detection
Load resistance
Machine learning
Physics
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Summary:With the increase of domestic electrical equipment, the incidence of electrical fires has also increased, and research on fault arc detection has become a hot topic today. In this paper, a method combining variational mode decomposition (VMD), and extreme learning machine (ELM) is proposed to detect arc faults accurately. The characteristic signals of the resistance, capacitance and inductive load under normal conditions and arc fault conditions were collected by experiments. Then, the current data was processed by variational mode decomposition (VMD). Due to the different spectral characteristics of normal mode, arc fault mode and switching transient mode, the intrinsic mode function (IMF) under arc fault mode can be selected. Finally, according to the characteristic of determined IMF components, a new arc fault criterion was proposed for general DC arc detection. The experimental results verified that the proposed method can detect arc faults accurately.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1486/6/062037