Research on Series Arc Fault Detection and Phase Selection Feature Extraction Method

Gao, H.; Wang, Zhiyong; Tang, Aixia; Han, Congxin; Guo, Fengyi; Li, Baifu

doi:10.1109/tim.2021.3080376

Cited by 20 publications

(10 citation statements)

References 16 publications

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“…The SVM algorithm extracts the skewness and the kurtosis of the collected signals according to a method for the performance comparison presented in [7]. This feature extraction is complete in the time domain.…”

Section: B Description Of the Algorithms Selected For Performance Com...mentioning

confidence: 99%

“…From the perspective of this study, the key to accurate arc fault detection is successful feature extraction; without effective feature extraction, even a sophisticated and well-refined ML algorithm can only lead to a low detection accuracy or overfitting. The voltage-current (VI) characteristics of arc faults are not decisive or constant, while the occurrences of arc faults are always accompanied by certain non-VI physical phenomena, including arc flash, magnetic flux, and radiation [7]. Therefore, many researchers have also attempted to detect arc faults through non-VI characteristics.…”

Section: Introductionmentioning

confidence: 99%

“…Zeng et al [26] proposed a method that extracted indices from FFT coefficients of harmonic components between 360 Hz and 800 Hz. Similarly, Ilman and Dzulkiflih [27] presented a method of detection by variations in peak values of discrete wavelet transform (DWT) coefficients, and Gao et al [7] introduced another method that extracted arc fault features by performing the singular value decomposition (SVD) on fractional Fourier transform (FRFT) coefficients of the first-order differential waveforms of the signals. Other extracted features, including the deviation of PCA eigenvectors between consecutive current signals [28] and SVD singular values obtained from Hankel matrices constructed from WA coefficients [6], were verified to be useful in their own scenarios.…”

Section: Introductionmentioning

confidence: 99%

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Fast-Fourier-Transform Enhanced Progressive Singular-Value-Decomposition Algorithm in Double Diagnostic Window Frame for Weak Arc Fault Detection

Shen

Wai

2022

IEEE Access

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In this study, a novel method that progressively applies the fastest form of singular-value decomposition (SVD) to extract nonperiodic arc-fault features is proposed in order to pursue a competent solution for AC weak arc fault detection. First, bus-current signals of the normal state and the arc-fault state are collected and normalized before being processed by progressive SVD (PSVD) to detect the discrepancy brought by comparatively stronger arc-fault nonperiodic components expressed in singular values. To provide a more comprehensive feature extraction for an enhanced accuracy, the fast Fourier transform (FFT) is incorporated for accumulating periodic variations caused by arc faults. Because weak arc faults are difficult to distinguish from normal signals when they start, a double diagnostic window frame (DDWF) is designed to reduce false negative errors. The effectiveness of each partial design of the method is verified by experiments with numerous load types and current amplitudes conducted on an industrial experimental platform. The proposed PSVD-FFT algorithm has achieved a satisfactory and consistent performance measured by both the detection accuracy and diagnosis time in all of the experiments. The proposed method is on average at least 10% more accurate than the selected methods for a parallel comparison (in total more than a thousand experimental cases), with a satisfactory range of execution time.INDEX TERMS Arc fault, singular value decomposition (SVD), fast Fourier transform (FFT), support vector machine (SVM).

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Section: B Description Of the Algorithms Selected For Performance Com...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Fast-Fourier-Transform Enhanced Progressive Singular-Value-Decomposition Algorithm in Double Diagnostic Window Frame for Weak Arc Fault Detection

Shen

Wai

2022

IEEE Access

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“…According to the advantage of each method, researchers combined different methods together, such as the wavelet and artificial neural network (ANN), for achieving maximum efficiency [8, 9]. In this study, we aim to address the problem of capacitor bank protection; therefore, it extends the work done by previous studies on fault occurrence [10, 11] and capacitor banks [12–17].…”

Section: Introductionmentioning

confidence: 99%

Behaviour analysis of H‐bridge high‐voltage capacitor banks fault on 230‐kV substation using discrete wavelet transform

Chiradeja,

Lertwanitrot,

Ngaopitakkul

et al. 2023

IET Generation Trans & Dist

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The protection of traditional high‐voltage capacitor banks relies on an unbalance relay which operates when an internal fuse is blown. However, the unbalance relay cannot indicate the cause of the fault. Thus, an operator wastes time and human resources investigating the fault issues. In this paper, a fault which occurred in a 230‐kV power system of Electricity Generating Authority of Thailand was observed by performing simulations using the Power Systems Computer Aided Design (PSCAD) program. The study system based on the double bus station and 72 MVAR capacitor banks was installed in the form of a back‐to‐back topology. Three scenarios were considered: normal condition, fault occurrence in one capacitor bank and fault occurrence in both capacitor banks. Current characteristics such as the current phase and difference in unbalance current were considered. In addition, discrete wavelet transform was applied to solve the ambiguity of current generated from the PSCAD. The authors’ results suggest that identifying fault events using a coefficient of wavelet is more efficient than relying on the current amplitude. The findings mentioned in this paper can be applied in a traditional power system protection scheme to enhance a system's reliability.

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“…The third is based on the analysis of the current signal, which is the leading research direction on arc fault at present. The analysis focuses mainly on the time–frequency features of the current signal 11 – 13 .…”

Section: Introductionmentioning

confidence: 99%

Series arc fault detection based on continuous wavelet transform and DRSN-CW with limited source data

Zhang

2022

Sci Rep

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When a series arc fault occurs in an indoor power distribution system, the temperature of arc combustion can be as high as thousands of degrees, which can lead to an electrical fire. Deep learning has developed rapidly in recent years and is widely used in fault diagnosis. The problem is that the sourced data is challenging to obtain, and few public data sources affect the application of deep learning models in arc fault diagnosis. In order to solve this problem, an arc fault detection method based on continuous wavelet transform and deep residual shrinkage network with the channel-wise threshold (DRSN-CW) is proposed. First, the grayscale images of source data features are obtained by continuous wavelet transform. Then, the feature images are data enhanced to construct the dataset. Finally, the DRSN-CW model is constructed and used to detect arc fault. The results show that the highest accuracy of arc fault detection is 98.92%, and the average accuracy is 97.72%. This method has excellent performance, which provides a new idea for arc fault detection.

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Research on Series Arc Fault Detection and Phase Selection Feature Extraction Method

Cited by 20 publications

References 16 publications

Fast-Fourier-Transform Enhanced Progressive Singular-Value-Decomposition Algorithm in Double Diagnostic Window Frame for Weak Arc Fault Detection

Fast-Fourier-Transform Enhanced Progressive Singular-Value-Decomposition Algorithm in Double Diagnostic Window Frame for Weak Arc Fault Detection

Behaviour analysis of H‐bridge high‐voltage capacitor banks fault on 230‐kV substation using discrete wavelet transform

Series arc fault detection based on continuous wavelet transform and DRSN-CW with limited source data

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