An arc fault diagnosis algorithm using multiinformation fusion and support vector machines.

ABSTRACT: Arc faults in low-voltage electrical circuits are the main hidden cause of electric fires. Accurate identification of arc faults is essential for safe power consumption. In this paper, a detection algorithm for arc faults is tested in a low-voltage circuit. With capacitance coupling and a logarithmic detector, the high-frequency radiation characteristics of arc faults can be extracted. A rapid method for computing the current waveform slope characteristics of an arc fault provides another characteristic. Current waveform periodic integral characteristics can be extracted according to asymmetries of the arc faults. These three characteristics are used to develop a detection algorithm of arc faults based on multiinformation fusion and support vector machine learning models. The tests indicat

SUBMITTER: Yang JH 

PROVIDER: S-EPMC6170584 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature