Series Arc Fault Detection Using Regular Signals and Time-Series Reconstruction

Jiang, Run; Zheng, Yuesheng

doi:10.1109/tie.2022.3165260

Cited by 18 publications

(3 citation statements)

References 27 publications

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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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Section: Introductionmentioning

confidence: 99%

Identifying DC Series and Parallel Arcs Based on Deep Learning Algorithms

2022

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“…On the other hand, an electric arcing usually occurs due to gas breakdown in electrical devices, such as circuit-breakers (CB) [28] and gas-insulated switchgear (GIS) [29]. However, the pantograph arcing, in contrast to these discharges, has distinct electric and electromagnetic properties because it is closely related to PC interaction.…”

Section: Introductionmentioning

confidence: 99%

Experimental Study on the Transient Disturbance Characteristics and Influence Factors of Pantograph–Catenary Discharge

Jin

et al. 2022

Energies

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The transient electromagnetic disturbance generated by arcing discharge between the pantograph and catenary can pose a significant risk to the safe operation of electrified railways. In order to better comprehend its properties, a pantograph–catenary discharge generating device is designed to simulate the discharge phenomenon with moving electrodes in this experimental investigation. The effects of the applied voltage, the gap distance, and the relative motion between the pantograph and catenary on the time- and frequency-domain features of the discharge current and electromagnetic field are investigated. The variation trends of pulse peak current, rise time, pulse repetition frequency, maximum amplitude, and characteristic frequency in the radiation spectrum are retrieved under varying experimental settings, and the effect mechanisms are derived from the physics of gas discharge. A dynamic discharge test is conducted in this study in order to further understand the effect of electrodes’ relative motion on discharge characteristics. The results indicate that lateral sliding motion of the pantograph along the track has a negligible effect on the transient discharge, whereas a faster vertical approaching motion between the pantograph and catenary generates a larger pulse current peak, a steeper rise front-edge, and a higher radiation intensity.

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“…A method based on high-frequency pulse components of coupled signals was used in the identification of AC series arc faults in residential buildings [13]- [14]. On the basis of this method, Run et al proposed short observation window singular value decomposition and reconstruction algorithm [15] and time series reconstruction algorithm [16] for enhancing high-frequency pulse components and realized series arc fault identification. However, due to the low amplitude and weak characteristics of AC arc currents of aviation cables, it is difficult to accurately identify the arc faults with timedomain features alone.…”

Section: Introductionmentioning

confidence: 99%

Detection of AC Arc Faults of Aviation Cables Based on H-I-W Three-Dimensional Features and CNN-LSTM Neural Network

et al. 2022

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Aiming at the difficulty in identifying subtle AC arcs in aviation cables, this paper proposes an arc fault detection method based on the combination of three-dimensional features and convolutional neural network-long short term memory (CNN-LSTM). Firstly, based on the SAE AS5692A standard, the vibration series test, cutting parallel test, and wet arc trajectory parallel test were respectively conducted and the arc current signals under four types of loads were collected to analyze the arc faults under different incentives. Then, the three-dimensional features of arc current including Hurst exponent, inter-harmonic variance, and wavelet energy entropy (H-I-W) were extracted with an improved algorithm so as to enhance the fault identification capability and overcome the limitation of single-dimensional feature detection. Finally, a grid search algorithm was used to find out the optimal parameters, and a three-dimensional reference input CNN-LSTM neural network was designed to detect arc faults. The experimental results showed that the average detection accuracy of the proposed method for the three AC arc faults respectively reached 98.52%, 99.23%, and 98.51%. The real-time performance of the proposed method was better than the comparison methods, proving the feasibility and effectiveness of the proposed method.

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Series Arc Fault Detection Using Regular Signals and Time-Series Reconstruction

Cited by 18 publications

References 27 publications

Identifying DC Series and Parallel Arcs Based on Deep Learning Algorithms

Identifying DC Series and Parallel Arcs Based on Deep Learning Algorithms

Experimental Study on the Transient Disturbance Characteristics and Influence Factors of Pantograph–Catenary Discharge

Detection of AC Arc Faults of Aviation Cables Based on H-I-W Three-Dimensional Features and CNN-LSTM Neural Network

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