Mechanisms of electric arc detection based on current waveform spectrum and incremental decomposition analysis

Dołęgowski, M.; Szmajda, M.

doi:10.15199/48.2016.11.15

Cited by 2 publications

(5 citation statements)

References 13 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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“…The key to detecting an arc efficiently is to define the following parameters of FFT: The sampling frequency, length, and shape of the measuring window. Moreover, on the basis of the FFT results, it is crucial to select the bandwidth, as the area within the arc introduces the most harmonics in reference to the background noise present there all the time [66], and this also applies to AC arc detection devices [69].…”

Section: Spectrum Analysis In Electric Arc Detectionmentioning

confidence: 99%

“…In this study, measurements were acquired from the oscilloscope, not directly from an analog-to-digital converter, so additional signal pre-processing had to be conducted compared with [66]. All samples had to be divided by the value of the least significant bit of the oscilloscope's ADC, which was 0.12 mV.…”

Section: Proposed Algorithm Of Incremental Decomposition Of the Signa...mentioning

confidence: 99%

“…The algorithm of incremental decomposition of the signal over time works in the time domain and has been proposed as a less computationally complex and time consuming alternative to the frequency or time-frequency analyses mentioned at the beginning of this paper. The proposed algorithm has been preliminarily tested for arc recognition at a low voltage of 150 V DC and at a low sampling frequency of 250 kS/s [66]. In this study, it was tested at different and higher voltages, for different values of current and various distances between the electrodes of the spark gap.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Algorithm for Fast DC Electric Arc Detection

Dołęgowski

Szmajda

2021

Energies

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Electric arcing is a common problem in DC power systems. To overcome this problem, the electric arc detection algorithm has been developed as a faster alternative to existing algorithms. The following issues are addressed in this paper: The calculation of the proposed algorithm of incremental decomposition of the signal over time; the computational complexity of Fast Fourier Transform (FFT) and the incremental decomposition; the test bench used to measure electric arcs at given parameters; the analysis of measurements using FFT; and the analysis of measurements using incremental decomposition. The parameters are the DC voltage, electric load, and width of the gap between electrodes. The results showed that the proposed algorithm allows for a faster calculation—about seven times faster than FFT—and cheaper implementation in electric arc detection devices than FFT.

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Mechanisms of electric arc detection based on current waveform spectrum and incremental decomposition analysis

Cited by 2 publications

References 13 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

A Novel Algorithm for Fast DC Electric Arc Detection

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