Identification of Series Arc Fault Occurred in the Three-Phase Motor With Frequency Converter Load Circuit via VMD and Entropy-Based Features

Zhi-yong, Wang; Han, Congxin; Gao, H.; Guo, Fengyi

doi:10.1109/jsen.2022.3221623

Cited by 9 publications

(8 citation statements)

References 30 publications

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“…G. Zou [6] obtained the arc fault current component by the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and proposed a highly identifiable feature mining method based on the maximal information coefficient and the feature change significance. 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%

“…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%

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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%

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

Liu,

Yang,

Wang

2024

Sensors

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“…Researchers call this strategy "extract and classify". Wavelet entropy clustering (WEC) [9][10][11][12][13][14][15] is a wellestablished technique for this approach. It is a versatile technique used in various fields.…”

Section: Introductionmentioning

confidence: 99%

“…It is a versatile technique used in various fields. These fields include biomedical research [9,10], monitoring strategies [11,12], imaging [13], and fault detection [14,15]. It utilizes wavelets and entropy mathematical functions to analyze TS data.…”

Section: Introductionmentioning

confidence: 99%

Wavelet Set Theory Enhanced Wavelet Entropy Clustering for Pattern Recognition

Saxena,

Mahapatra,

Rizvi

2024

Preprint

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“…To name a few, reference [3] used improved complete ensemble empirical mode decomposition adaptive noise (ICEEMDAN) to obtain the IMF component of the signal and constructed detection variables to distinguish between arc fault and normal states. Reference [4] applied the entropy feature of the variational modal decomposition (VMD) as the indicator for arc fault detection. Reference [5] utilized the correlation coefficient of the signal as the indicator for the detection of an arc with different loads.…”

Section: Introductionmentioning

confidence: 99%

Multi-Branch Line Fault Arc Detection Method Based on the Improved Northern Goshawk Optimization Adaptive Base Class LogitBoost Algorithm

Wang,

Zhao

2024

Energies

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In low-voltage AC distribution systems, when a series arc fault occurs in a branch with multiple loads operating in parallel, it will be significantly more difficult to identify. Existing arc fault detection methods make it difficult to effectively detect faults occurring in the lower-level branch. This study introduces a novel series arc fault detection approach based on the improved northern goshawk optimization adaptive base class LogitBoost (INGO-ABCLogitBoost) algorithm. Considering the zero-rest, intermittent, and random fluctuation and high-frequency features of the arc current, the zero-rest coefficient, discrete coefficient, harmonic amplitude, and wavelet entropy are proposed to establish the high-dimensional feature matrix of the arc current. The ReliefF feature selection algorithm is used to optimize feature quality and decrease feature dimensionality. Subsequently, the ABCLogitBoost fault detection model is proposed, with the INGO algorithm applied to optimize the model parameters, thus enhancing the model’s diagnostic capabilities. The efficacy of the proposed diagnostic model is validated through the construction of a multi-load arc simulation system. The simulation results show that the overall fault diagnosis accuracy of the proposed method reaches 99.01% and can effectively identify the fault load types, which helps to locate the fault location.

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Identification of Series Arc Fault Occurred in the Three-Phase Motor With Frequency Converter Load Circuit via VMD and Entropy-Based Features

Cited by 9 publications

References 30 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

Wavelet Set Theory Enhanced Wavelet Entropy Clustering for Pattern Recognition

Multi-Branch Line Fault Arc Detection Method Based on the Improved Northern Goshawk Optimization Adaptive Base Class LogitBoost Algorithm

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