Deep learning-based series AC arc detection algorithms

Park, Chang-Ju; Dang, Hoang-Long; Kwak, Sangshin; Choi, Seungdeog

doi:10.1007/s43236-021-00299-5

Cited by 14 publications

(2 citation statements)

References 28 publications

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“…Models are trained using a large set of labeled data and neural network architectures containing many layers, such as input, hidden, and output layers. Each layer contains various neurons; the output of one neuron in the n th layer is the input of another neuron in the n+1 th layer [32]. The hidden configurations of DL algorithms (deep neural network (DNN), gated recurrent unit (GRU), and long-short term memory (LSTM)) are listed in Table 2.…”

Section: A Advanced Learning Algorithms Structuresmentioning

confidence: 99%

Parallel DC Arc Failure Detecting Methods Based on Artificial Intelligent Techniques

2022

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Section: A Advanced Learning Algorithms Structuresmentioning

confidence: 99%

Parallel DC Arc Failure Detecting Methods Based on Artificial Intelligent Techniques

2022

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“…There are various neurons in each layer. The input of a given neuron in the n th layer could be the output of a neuron in the n-1 th layer [35]. Table 2 lists the hidden structures of DL methods (Gated Recurrent Unit (GRU), Long-Short Term Memory (LSTM) and Deep Neural Network (DNN)) utilized in this study.…”

Section: A Artificial Learning Principlesmentioning

confidence: 99%

Identifying DC Series and Parallel Arcs Based on Deep Learning Algorithms

2022

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Arc phenomena are usually related to the undesired disengagement of two electrical connections. The emission power discharge from the failure arc may damage wiring and can present a fire hazard. Numerous studies have been proposed to detect arc events and quickly isolate them from an electrical system. DC arc faults are often sorted into two types: series and parallel arcs. A series arc may be the outcome of discharging links in electrical wiring. By contrast, the parallel arc occurs between two electric wires, or between a link and a ground, owing to contamination or poor isolation. The currents in a system with an arc fault are considerably greater when the arc parallel in nature than when the arc is series in nature. In this paper, the electric activities of a network are investigated for the duration of series and parallel arc failures in both the time and frequency domains. The arcing behavior investigated is selected to allow for the identification of series and parallel arcs. The sorting of electrical arcs in an accurate and reliable manner is useful for electrical protection schemes. The identification process used here is based on data related to different domains, such as load current and voltage. In this study, eight learning techniques are investigated with the aim of detecting series and parallel arc faults. The arc behaviors were studied in the various domains. We used the load current and voltage characteristics as an statistic for categorizing a given arc failure. This study could be beneficial to enhance the stability and reliability of arc-fault detectors.

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