Research on Low-Voltage Arc Fault Detection Based on BP Neural Network

Ren Cheng Zhang; Kai Yang; Qi Yong Wu; Jian Hong Yang

doi:10.4028/www.scientific.net/AMM.651-653.499

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 651-653Research on Low-Voltage Arc Fault Detection Based...

Research on Low-Voltage Arc Fault Detection Based on BP Neural Network

Abstract:

In electrical fires, arc fault is one of the important reasons. In virtue of cross talk, randomness and weakness of arc faults in low-voltage circuits, very few of techniques have been well used to protect loads from all arc faults. Thus, a novel detection method based on BP neural network was developed in this paper. When arc faults occur in circuits, current integrations of cycles were variable and erratic. However, current integrations of cycles would also vary while the working conditions of circuits change. To better discriminate the current integrations, four characteristics were extracted to represent their differences through chain code. Based on these characteristics, BP neural network was used to distinguish arc faults from normal operations. The validity of the developed method was verified via an experimental platform set up. The results show that arc faults are well detected based on the developed method.

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Periodical:

Applied Mechanics and Materials (Volumes 651-653)

Pages:

499-502

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.651-653.499

Citation:

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Online since:

September 2014

Authors:

Ren Cheng Zhang*, Kai Yang, Qi Yong Wu, Jian Hong Yang

Keywords:

Arc Fault Detection, BP Neural Network (BPNN), Chain Code, Current Integration

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* - Corresponding Author

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