Parallel DC Arc Failure Detecting Methods Based on Artificial Intelligent Techniques

Dang, Hoang-Long; Kwak, Sangshin; Choi, Seungdeog

doi:10.1109/access.2022.3157298

Cited by 16 publications

(3 citation statements)

References 31 publications

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“…Hence, the sample rate of 250 kHz was considered to be sufficiently high to ensure a balance between efficiency and execution time. Additionally, the recent arc fault research selected similar data durations (arrangements of 2, 3, or 4 ms periods) [21][22][23][24][25]. Therefore, this study chooses the window duration at a 2 ms period.…”

Section: Properties Of DC Arcing Failuresmentioning

confidence: 99%

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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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Section: Properties Of DC Arcing Failuresmentioning

confidence: 99%

“…[19][20][21][22], numerous AI models were employed to diagnose series arcing events using different characteristics as inputs. The adoption of AI algorithms for parallel arc diagnosis was proposed in [23]. Previous studies have illustrated performance comparisons among various AI algorithms in DC networks [24].…”

Section: Introductionmentioning

confidence: 99%

Identifying DC Series and Parallel Arcs Based on Deep Learning Algorithms

2022

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“…The traditional arc-optic grounding fault identification method of the distribution network is based on the steadystate or transient electrical parameters and the set threshold (Chen et al, 2021), and in the fault identification method based on the transient electrical parameters, the characteristic parameters of the typical fault type are first extracted, including wavelet transform (WT) (Qin et al, 2018;Lin et al, 2019;Wei et al, 2020a), empirical mode decomposition (EMD) (Guo et al, 2019;Cai and Wai, 2022), and S transformation (ST) (Peng et al, 2019); Then, the arc ground fault is classified and identified by the pattern recognition method, mainly including the neural network method (Siegel et al, 2018;Du et al, 2019a), the Support Vector Machine (SVM) (Xia et al, 2019;Dang et al, 2022), the fuzzy control method (Zeng et al, 2016), the clustering (Wang et al, 2015), etc., in addition, the high-precision current transformer can be used to improve the fault identification ability (Paul, 2015), but the detection cost is also significantly increased.…”

Section: Introductionmentioning

confidence: 99%

Research on arc grounding identification method of distribution network based on waveform subsequence segmentation-clustering

Li²,

Long³

et al. 2023

Front. Energy Res.

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The traditional method of detecting fault current based on threshold judgment method is limited by the current size and is easily disturbed by noise, and it is difficult to adapt to the arc ground fault detection of the distribution network. Aiming at this problem, this paper proposes a single-phase arc-optic ground fault identification method based on waveform subsequence splitting fault segmentation, combined with three-phase voltage-zero sequence voltage waveform feature extraction clustering. First of all, the waveform fault segment is segmented and located, secondly, the characteristic indexes of the time domain and frequency domain of the combined three-phase voltage-zero sequence voltage waveform are established, and the multidimensional feature distribution is reduced by the principal component analysis method, and finally, the characteristic distribution after the dimensionality reduction is identified by the K-means clustering algorithm based on the waveform subsequence. Experimental results show that the arc light grounding fault identification method proposed in this paper achieves 97.12% accurate identification of the test sample.

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