Serial Arc Fault Detection Through Wavelet Transform and Support Vector Machine

Rocha, G; Pulz, Lucas T. C.; Gazzana, Daniel S.

doi:10.1109/eeeic/icpseurope51590.2021.9584530

Cited by 4 publications

(3 citation statements)

References 17 publications

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“…Regarding the aspect of feature extraction of an arc fault current and voltage signals, Gustavo S. da Rocha [3] took the loop current as the analysis object, and combined wavelet decomposition with the support vector machine (SVM) to realize the detection of the SAF. Yu [4] used improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) to decompose the arc fault current data, and then constructed the detection variables to distinguish the arc fault from the normal state by selecting the intrinsic mode function (IMF) and calculating its variance.…”

Section: Introductionmentioning

confidence: 99%

“…Z. Wang [7] proposed an SAF identification method based on the variational mode decomposition (VMD) and support vector machine. Although the feature extraction of arc fault data has been carried out in References [3][4][5][6][7], there are still some problems, such as the modal aliasing or average error of the set in the data processed by wavelet decomposition, CEEMDAN, RQA and other algorithms.…”

Section: Introductionmentioning

confidence: 99%

“…In References [3][4][5][6][7][8][9][10][11][12][13][14], good results have been achieved in the aspects of arc fault signal preprocessing, feature analysis and detection model establishment, but there are still the following shortcomings. In References [3][4][5][6][7], when the fault data are preprocessed, the empirical mode decomposition (EMD) correlation algorithms have problems such as modal aliasing or difficulty in aligning IMF components, which will have a certain impact on the diagnosis results. In References [8][9][10][11], the deep learning algorithm performs well in arc fault detection, but there is still some room for optimization.…”

Section: Introductionmentioning

confidence: 99%

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Series Arc Fault Detection under Vibration Condition Based on N-M-M-B

Liu,

Yang,

Wang

2024

Sensors

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Under the conditions of a mechanical fault in a motor, mechanical vibration of a specific frequency can be generated. The electrical contact points directly connected to the motor can vibrate at the same frequency. The electrical contact points with poor contact can easily produce a series arc fault under vibration conditions, which affects the reliability of the power supply. In order to detect the series arc fault under different vibration conditions, the arc fault generator is connected between the back end of the frequency converter and the motor. An arc fault experiment under different vibration conditions was carried out and the fault phase current and arc voltage signals were collected. In this paper, the noise-assisted multivariate empirical mode decomposition and the correlation coefficient between each intrinsic mode function are used to select the fault feature signals. Then, the reconstructed signal is input into the series arc fault model combining a multi-scale convolutional neural network and a bidirectional long short-term memory network for training. The research results show that the series arc fault detection method proposed in this paper can effectively detect the series arc fault and can preliminarily identify the type of motor fault causing the mechanical vibration of the motor; the model has good noise immunity and generalization.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Series Arc Fault Detection under Vibration Condition Based on N-M-M-B

Liu,

Yang,

Wang

2024

Sensors

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Series arc‐fault diagnosis using convolutional neural network via generalized S‐transform and power spectral density

Zhang,

Qin

2024

IET Generation Trans & Dist

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It is difficult to identify an arc fault accurately when the loads on the user side are more complicated, which hinders the development of low‐voltage monitoring and pre‐warning inspection. This study acquired a series of arc‐fault signals according to IEC 62606. The main time‐frequency features were strengthened with high efficiency by applying the generalized S‐transform to them with a bi‐Gaussian window. Further, the power spectrum density determination allowed for the detection of imperceptible high‐frequency harmonic energy reflections, thus increasing the rate of arc‐fault diagnosis and making it suitable for arc‐fault monitoring of non‐linear loads. The final samples were trained and classified using a 2D convolutional neural network and the overall accuracy of identification was observed to be 98.13%, which involved various domestic loads, thus providing a reference for follow‐up arc‐fault monitoring and inspection research.

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Feature Extraction and Detection Method of Series Arc Faults in a Motor With Inverter Circuits Under Vibration Conditions

Yan-li

Zhang

et al. 2024

IEEE Trans. Ind. Electron.

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Serial Arc Fault Detection Through Wavelet Transform and Support Vector Machine

Cited by 4 publications

References 17 publications

Series Arc Fault Detection under Vibration Condition Based on N-M-M-B

Series Arc Fault Detection under Vibration Condition Based on N-M-M-B

Series arc‐fault diagnosis using convolutional neural network via generalized S‐transform and power spectral density

Feature Extraction and Detection Method of Series Arc Faults in a Motor With Inverter Circuits Under Vibration Conditions

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