A Novel Algorithm for Fast DC Electric Arc Detection

Dołęgowski, M.; Szmajda, M.

doi:10.3390/en14020288

Cited by 7 publications

(5 citation statements)

References 67 publications

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“…In the formulas, 𝑁 𝐷𝑐𝑜𝑛𝑣 is the amount of calculation in the depth separable convolution process; 𝑃 𝐷𝑐𝑜𝑛𝑣 is the parameter quantity in the process of depth separable convolution; the ratio of the amount of calculation, the number of parameters in the depth separable convolution and the standard convolution can be obtained by Formulas (10) and (11). Figure 5a describes the deep convolution process, which is grouped according to the channel of the input feature map.…”

Section: Depth Separable Convolutionmentioning

confidence: 99%

“…Especially with the increasing proportion of non-linear loads in lowvoltage distribution systems [9], the normal current under certain operating conditions is likely to be very close to the time domain and frequency domain of the fault arc, which easily makes the arc detection wrong and missed. Michal et al [10,11] proposed an algorithm for the incremental decomposition of a signal over time, which is about seven times faster than the FFT and cheaper to implement than FFT in arc fault detection equipment.…”

Section: Introductionmentioning

confidence: 99%

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Fault Arc Detection Based on Channel Attention Mechanism and Lightweight Residual Network

et al. 2023

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An arc fault is the leading cause of electrical fire. Aiming at the problems of difficulty in manually extracting features, poor generalization ability of models and low prediction accuracy in traditional arc fault detection algorithms, this paper proposes a fault arc detection method based on the fusion of channel attention mechanism and residual network model. This method is based on the channel attention mechanism to perform global average pooling of information from each channel of the feature map assigned by the residual block while ignoring the local spatial data to enhance the detection and recognition rate of the fault arc. This paper introduces a one-dimensional depth separable convolution (1D-DS) module to reduce the network model parameters and shorten the time of single prediction samples. The experimental results show that the F1 score of the network model for arc fault detection under mixed load conditions is 98.07%, and the parameter amount is reduced by 46.06%. The method proposed in this paper dramatically reduces the parameter quantity, floating-point number and time complexity of the network structure while ensuring a high recognition rate, which improves the real-time response ability to detect arc fault. It has a guiding significance for applying arc fault on the edge side.

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Section: Depth Separable Convolutionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fault Arc Detection Based on Channel Attention Mechanism and Lightweight Residual Network

et al. 2023

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“…Reference [12] summarized the series arc fault (SAF) detection technologies for photovoltaic systems using the following four categories: remote detection techniques [13], time-domain methods, frequency-domain methods, and hybrid techniques. Among these four general techniques, both the time-domain [14] and frequency-domain [15,16] detection methods are based on the current and voltage signals in the circuit, and hybrid detection technology combines the time-domain and frequency-domain signal characteristics [17,18]. The main fault diagnosis methods are spectrum analysis, wavelet analysis, and artificial intelligence [19], and arc fault detection technology has seen improvements in terms of its feature extraction and fault detection algorithms in recent years.…”

Section: Introductionmentioning

confidence: 99%

Fast Arc Detection Technology Based on Fractal Dimension for SADA Slip Ring in Satellite Power System

Meng,

Zhang,

Zhang

et al. 2024

Energies

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The solar array drive assembly (SADA) slip ring is a critical link that provides electrical power and electric signal transmission between solar arrays and satellite power systems, which is prone to arc faults in the space environment. If these arc faults cannot be detected and eliminated quickly enough, they will seriously threaten the safety of the satellite power system and the satellite. In this paper, a fast arc detection method based on fractal dimension is proposed that adapts to different operating modes of power systems. The detection method collects the current differential signal data flowing through the SADA slip ring, and, according to the trend of the fractal dimension change in this signal, the fault identification algorithm is designed for different operating modes of the power system to achieve real-time and rapid identification of arc faults. Finally, the effectiveness of the proposed method is demonstrated using test data under several different fault conditions.

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“…Photovoltaic DC arc faults pose a significant safety hazard in photovoltaic systems due to their unique characteristics and severe consequences [24]. These faults often occur in the DC combiner box or DC distribution box, where the large number of photovoltaic cell strings increases the energy density of any arc fault [25].…”

Section: Introductionmentioning

confidence: 99%

Progress of Photovoltaic DC Fault Arc Detection Based on VOSviewer Bibliometric Analysis

Song,

Lu,

et al. 2024

Energies

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This paper presents a review of research progress on photovoltaic direct current arc detection based on VOSviewer bibliometric analysis. This study begins by introducing the basic concept and hazards of photovoltaic DC arcing faults, followed by a summary of commonly used arc detection techniques. Utilizing VOSviewer, the relevant literature is subjected to clustering and visualization analysis, offering insights into research hotspots, trends, and interconnections among different fields. Based on the bibliometric analysis method of VOSviewer software, this paper analyzes the articles published in the last 10 years (2014–2023) on photovoltaic DC fault diagnosis. We analyzed the specific characteristics of 2195 articles on arc failures, including year of publication, author, institution, country, references, and keywords. This study reveals the development trend, global cooperation model, basic knowledge, research hotspots, and emerging frontier of PV DC arc. Future research directions and development trends for photovoltaic DC arc detection are proposed which provides valuable references for further studies and applications in this domain. This comprehensive analysis indicates that photovoltaic DC arc detection technology is expected to find broader applications and greater promotion in the future.

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A Novel Algorithm for Fast DC Electric Arc Detection

Cited by 7 publications

References 67 publications

Fault Arc Detection Based on Channel Attention Mechanism and Lightweight Residual Network

Fault Arc Detection Based on Channel Attention Mechanism and Lightweight Residual Network

Fast Arc Detection Technology Based on Fractal Dimension for SADA Slip Ring in Satellite Power System

Progress of Photovoltaic DC Fault Arc Detection Based on VOSviewer Bibliometric Analysis

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