An arc fault diagnosis algorithm using multiinformation fusion and support vector machines

Yang, Jianhong; Fang, Huaiying; Zhang, Rencheng; Yang, Kai

doi:10.1098/rsos.180160

Cited by 5 publications

(2 citation statements)

References 14 publications

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“…In addition, some studies deal with the current signal using signal analysis methods to obtain the feature vector and construct the mapping relationship between the feature vector and the series arc fault. By inputting the feature vector into the classifier, such as a support vector machine [25], a convolutional neural network [26], a recurrent neural network [27], random forest [28], etc., an arc fault identification model is established. Commonly used signal analysis methods include Fourier transform [29], Chirp Z-Transform [30], wavelet transform [31], empirical mode decomposition [32], etc.…”

Section: Introductionmentioning

confidence: 99%

Non-Intrusive Arc Fault Detection and Localization Method Based on the Mann–Kendall Test and Current Decomposition

Jiang

Liu

Yang³

et al. 2023

Energies

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In recent years, electrical fires caused by arc faults have been increasing, seriously affecting the safety of people’s lives and property. Considering the complex arc fault characteristics of actual low-voltage users, the non-intrusive arc fault detection and localization method is studied. First, the characteristics of arc current waveforms are analyzed, and event detection based on the Mann–Kendall Test is performed for the difference between the current waveforms of two adjacent cycles, rather than using the current waveforms directly. Then, the current waveforms of the two segments are calculated via subtraction to obtain the current waveform of the electric appliances causing the event. A current feature parameter database of the normal and arc currents is constructed via harmonic analysis, and a multi-appliance current decomposition model considering the sparse operation characteristics of appliances is established; thus, the arc localization problem is transformed into an optimization problem. Finally, a genetic algorithm is used to optimize the differential current decomposition results, and then, locate the arc fault. A household arc fault simulation experiment is carried out for the common electric appliances of actual low-voltage users. The experimental results show that the proposed non-intrusive arc fault detection and localization method is effective.

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Section: Introductionmentioning

confidence: 99%

Non-Intrusive Arc Fault Detection and Localization Method Based on the Mann–Kendall Test and Current Decomposition

Jiang

Liu

Yang³

et al. 2023

Energies

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“…Arc fault protection devices require a series of arc fault tests to ensure their safety and fault detection ability. An arc-fault generating device can restore the actual behavior of a low-voltage circuit during an arc fault [1][2][3] . Carbonized cables are primarily utilized to simulate arc faults caused by the carbonization of wires due to insulation breakdown and aging.…”

Section: Introductionmentioning

confidence: 99%

Improved Control Strategy for Carbonized Cable Preparation in Low-Voltage Arc Fault Test

Du,

Xu,

Chen

2023

Chin. J. Electr. Eng.

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Outdated testing methods hinder the success rate of carbonized cable preparation in low-voltage arc fault tests, leading to incomplete tests and high failure rates. To address this issue, we finely categorized the preparation results of carbonized cable specimens by analyzing the experimental phenomena during the carbonization process and assessing the impact of high-voltage energization time on the outcomes, presenting a process control strategy aimed at optimizing the preparation results of carbonized cable specimens. This method utilizes three periodic moving algorithms (root-mean-square, average, and shoulder percentage) to classify the cable specimens into four preparation categories: open-circuit carbonization, under-carbonization, short-circuit carbonization, and successful carbonization. The high-voltage energization time during carbonization or secondary carbonization was adjusted to optimize the preparation of the carbonized cables by considering different discrimination outcomes. Finally, the proposed method was tested on a purpose-built carbonized cable experimental platform, which confirmed its effectiveness in differentiating the preparation outcomes of the carbonized cable specimens and improving the success rate of the carbonized cable preparation. The proposed method has significant potential for application in low-voltage arc fault test systems.

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Novel Series Arc Fault Detector Using High-Frequency Coupling Analysis and Multi-Indicator Algorithm

Bao¹,

Jiang²,

Gao³

2019

IEEE Access

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In the field of arc fault detection, it is universally acknowledged that it is very hard to judge whether there is an arc fault through signals of the main line when a masking load (such as air compressors, lamps with dimmers, and so on) is in parallel with a resistive load, which always tends to be a fire hazard. Meanwhile, it is annoying that the normal currents of some appliances are very similar to the arcing ones. In this paper, we have found the principles of a novel detection method called high-frequency coupling, putting the neutral line (N) and the live line (L) through the current transformer (CT), which results in asymmetrical distribution of magnetic flux in the core and the only high-frequency components left in the secondary output of the CT. So it is possible for series arc fault detectors to be free from the masking loads and distinguish between the arcing and the nonarcing clearly. Thanks to this convenient method, an arc fault detector based on the microcontroller unit (MCU) has been proposed to detect arc faults effectively by means of simple multi-indicators. The experimental results show the accuracy of arc fault recognition, in all the masking tests, can reach a high level and the detector can detect an arc fault within a short time. INDEX TERMS Series arc fault, high-frequency coupling, asymmetrical distribution, magnetic flux, multi-indicators, detector, MCU, masking tests.

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An arc fault diagnosis algorithm using multiinformation fusion and support vector machines

Cited by 5 publications

References 14 publications

Non-Intrusive Arc Fault Detection and Localization Method Based on the Mann–Kendall Test and Current Decomposition

Non-Intrusive Arc Fault Detection and Localization Method Based on the Mann–Kendall Test and Current Decomposition

Improved Control Strategy for Carbonized Cable Preparation in Low-Voltage Arc Fault Test

Novel Series Arc Fault Detector Using High-Frequency Coupling Analysis and Multi-Indicator Algorithm

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