A Study of Detection Algorithms and Analysis Series Arc of Quasi-arc Load

Lim, Jong-Ung; Ju, Jae-Yeon; Kang, Kyoung-Pil; Bang, Sun-Bae; Choe, Gyu-Ha

doi:10.5207/jieie.2014.28.7.081

Cited by 2 publications

(2 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…And the ARC CPU determines an arc fault after analyzing both the FFT on load current and the arc fault pulse obtained from the RF current sensor. [8][9][10][11] Figure 2 demonstrates a flow chart of failure detection of an autonomous safety device. According to the flow chart, an arc fault can be determined for the five types of sensors displayed in Figure 1 by voltage, load current, and RF current.…”

Section: R E T R a Cmentioning

confidence: 99%

See 1 more Smart Citation

A study on the development of an autonomous electrical safety management service using an IoT-based smart outlet

Lee¹,

Moon²,

Kim³

et al. 2019

International Journal of Electrical Engineering & Education

View full text Add to dashboard Cite

At present, individual households within a multi-family residential are at susceptible to electrical accidents. As if in a blind spot, they cannot be covered under electrical safety management. This paper presents issues about an autonomous electrical safety management service which uses smart outlets to ensure electrical safety for individual households. Risk factors surrounding electrical incidents occurring in a mashup tech-based environment were analyzed and a smart outlet equipped with a prediction technique was developed. In addition to this, an autonomous electrical safety management service scenario was developed to handle mechanical failures in autonomous electrical safety devices which run on IoT-based smart outlets. By utilizing such an autonomous electrical safety management service model, technology for electrical safety security was developed for individual households or each residential unit in multi-dwelling unit. The correlation among voltage, current, and zero phase current was analyzed to identify the risk factors of electrical accidents and to predict accidents. In order to detect arc faults, a new prediction technique was developed. The technique is based on the mashup technology after analyzing waveform and FFT derived from the correlation among voltage, current, and zero phase current. In particular,

show abstract

Section: R E T R a Cmentioning

confidence: 99%

“…And the ARC CPU determines an arc fault after analyzing both the FFT on load current and the arc fault pulse obtained from the RF current sensor. 8–11…”

Section: Development Of An Iot-based Smart Outletmentioning

confidence: 99%