DC Series Arc Fault Detection Algorithm for Distributed Energy Resources Using Arc Fault Impedance Modeling

Park, Hwa-Pyeong; Chae, Su Young

doi:10.1109/access.2020.3027869

Cited by 28 publications

(14 citation statements)

References 25 publications

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“…3) Emulated Electrical System: As a middle ground between a simplified circuit and an actual real electrical system, the solution of an emulated system has been put forward. This can be in the form of a hardware emulator, such as the PV emulators used in [15], [79] (concept shown in Fig. 9), which are currently available as off the shelf equipment.…”

Section: B Electrical Systemsmentioning

confidence: 99%

“…This presents a challenge as conventional over-current protection techniques are unable to detect and isolate a DC series arc fault [10]. This has led to a growing interest in the development of arc fault detection devices (AFDDs), which has been an area of active research with novel arc fault detection approaches relying on time-domain techniques [11], [12], frequency-domain techniques [13], [14], or a combination of the two [15]- [17]. More recently, the application of data-driven approaches (or artificial intelligence) for identification of such faults has been explored [18], [19].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Review of DC Series Arc Fault Testing Methods and Capability Assessment of Test Platforms for More-Electric Aircraft

Psaras¹,

Seferi

Syed

et al. 2022

IEEE Trans. Transp. Electrific.

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In the new era of increasingly electric aircraft, the need for reliable and safe electrical systems is more important than ever. In addition, the wide scale adoption of DC distribution is considered a key enabling technology for more efficient aircraft operation. In this context, arc fault detection devices have become a topic of interest for the aviation industry with ongoing research to characterize the impact and adequately protect against severe DC series arc faults. Although DC arc faults have been widely investigated for utility applications (such as solar photo-voltaic systems), direct adoption of current practices for validating arc detection devices is not straightforward due to the distinct aircraft operating environment. This paper provides a first of its kind, landscaping exercise of published series arc fault testing based on factors associated with aircraft applications which have the potential to influence the arc characteristics. In addition, an appraisal and associated gap analysis of published arc test platforms is undertaken in order to assess their suitability to support in-depth testing of the impact and mitigation of series arcs within future aircraft DC electrical systems and identify future testing needs in particular to better facilitate a comprehensive performance validation of new arc fault detection devices.

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Section: B Electrical Systemsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Review of DC Series Arc Fault Testing Methods and Capability Assessment of Test Platforms for More-Electric Aircraft

Psaras¹,

Seferi

Syed

et al. 2022

IEEE Trans. Transp. Electrific.

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show abstract

“…Liu et al [18] introduced a method that detected arc faults via variational mode decomposition (VMD) in selected frequency bands. Comparably, Park and Chae [19] illustrated another method based on the deviation of the current in the time domain coupled with high frequency spectra extracted by the FFT. Other feature extraction methods in this category include parameters calculated by the standard deviation of the current and voltage [20], as well as changes in the voltage and current amplitudes combined with differences in the WT coefficients [21].…”

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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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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“…Under actual operating conditions, the presence of an arcing fault could significantly contribute to service interruption [4]. Research has generally been conducted on arcing fault detection, such as in [5] [6] [7][8] [9]. However, quite a few effective methods for detecting and classifying incipient arcing have been discussed.…”

Section: Introductionmentioning

confidence: 99%

Arcing fault diagnosis using enhanced cross-correlation technique

Amirruddin

Adzman

Affendi

et al. 2022

J. Phys.: Conf. Ser.

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This study explored the potential use of cross-correlation as a technique for detecting arcing faults in a power system distribution network. The cross-correlation technique was employed to investigate the effect of each antenna placement as a detection device, time difference of arrival (TDOA), time delay, and correlation magnitude of arcing signals detected during on-line arcing fault measurement. The arcing fault was detected using four antennas that had been set up around the arc source point in a high voltage (HV) laboratory. The measurements were taken using a digital oscilloscope. For precise results, the Discrete Wavelet Transform (DWT) denoising technique combined with cross-correlation (CC) technique were applied using MATLAB software to identify the arcing signals detected in order to diagnose the differentiation between noisy and real arcing fault signals. Further assessment was carried out by performing a cross-correlation technique on the real arcing signals obtained to find the similarities and arrival time’s delay between single arcing signals’ placement. The outcome shows that all measurements including the time difference of arrival (TDOA), correlation magnitude, time delay, and antennas’ placement towards the arcing source point are valuable in determining the arcing signals detected precisely.

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DC Series Arc Fault Detection Algorithm for Distributed Energy Resources Using Arc Fault Impedance Modeling

Cited by 28 publications

References 25 publications

Review of DC Series Arc Fault Testing Methods and Capability Assessment of Test Platforms for More-Electric Aircraft

Review of DC Series Arc Fault Testing Methods and Capability Assessment of Test Platforms for More-Electric Aircraft

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

Arcing fault diagnosis using enhanced cross-correlation technique

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