A New Method for Detecting Series Arc Fault in Photovoltaic Systems Based on the Blind-Source Separation

Ahmadi, Mohammad Bagher; Samet, Haidar; Ghanbari, Teymoor

doi:10.1109/tie.2019.2922926

Cited by 43 publications

(25 citation statements)

References 27 publications

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“…A conclusive experiment on all of the experimental data is performed to simulate a realistic working environment when the loads are switched, and the bus currents are varied in amplitudes. The results of the proposed method presented in this study are also compared with the results obtained by existing works on the same experimental data, including the support vector machine (SVM), the principal-component-analysis-SVM (PCA-SVM) in [28], the empirical-mode-decomposition probabilistic-neural-network (EMD-PNN) [25], and the progressive singular-value decomposition (PSVD), which is the previous version of the proposed method. All algorithms are programmed in MATLAB 2020 and executed on a laptop with an Intel i7-7700HQ CPU and 16 GB of RAM.…”

Section: Experiments and Analyses A Experimental Setupmentioning

confidence: 99%

“…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. This category has more strength in weak arc fault detection because after secondary processing and magnification of heuristic arc-fault VI characteristics, the algorithms are capable of learning to recognize arc faults from the given data and training models.…”

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: Experiments and Analyses A Experimental Setupmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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“…When the DC arc faults are initiated, several abnormal behaviors can be used to diagnose an arc event, such as current fluctuations and rapid changes in light and/or heat output. These abnormal phenomena can be used to detect DC arcs [6][7][8][9][10][11]. However, the investigation of parallel arc in DC systems is still at a primitive step [12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Identifying DC Series and Parallel Arcs Based on Deep Learning Algorithms

2022

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Arc phenomena are usually related to the undesired disengagement of two electrical connections. The emission power discharge from the failure arc may damage wiring and can present a fire hazard. Numerous studies have been proposed to detect arc events and quickly isolate them from an electrical system. DC arc faults are often sorted into two types: series and parallel arcs. A series arc may be the outcome of discharging links in electrical wiring. By contrast, the parallel arc occurs between two electric wires, or between a link and a ground, owing to contamination or poor isolation. The currents in a system with an arc fault are considerably greater when the arc parallel in nature than when the arc is series in nature. In this paper, the electric activities of a network are investigated for the duration of series and parallel arc failures in both the time and frequency domains. The arcing behavior investigated is selected to allow for the identification of series and parallel arcs. The sorting of electrical arcs in an accurate and reliable manner is useful for electrical protection schemes. The identification process used here is based on data related to different domains, such as load current and voltage. In this study, eight learning techniques are investigated with the aim of detecting series and parallel arc faults. The arc behaviors were studied in the various domains. We used the load current and voltage characteristics as an statistic for categorizing a given arc failure. This study could be beneficial to enhance the stability and reliability of arc-fault detectors.

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“…Fallen trees can cause a high or medium voltage to arc over and cause intermittent earth leakage. Continuing on to the low voltage systems, arc fault detection is a hot topic in household mains [6][7][8][9][10][11][12][13][14][15] protection, photovoltaic arrays, and DC microgrids [16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31]. To ensure safety in that regard, several standards have been developed, including UL 1699 for household mains arc fault detection devices and UL 1699B for DC arc detection in photovoltaic systems [32].…”

Section: Introductionmentioning

confidence: 99%

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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A New Method for Detecting Series Arc Fault in Photovoltaic Systems Based on the Blind-Source Separation

Cited by 43 publications

References 27 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

Identifying DC Series and Parallel Arcs Based on Deep Learning Algorithms

A Novel Algorithm for Fast DC Electric Arc Detection

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